phd level books

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What are some good graduate-level econometrics books for someone with a strong mathematics background?

Related: Book recommendations on empirical methods in economic research and econometrics?

I would like to focus mainly on graduate texts in Econometrics . From the question above, I gather that Wooldridge's text is nice.

In terms of "strong math background," I did my undergrad in Statistics, consisting of two semesters each of linear algebra, real analysis, and abstract algebra, along with a measure-theoretic probability course.

What are some econometrics texts that you would recommend for me? I learned some intro econometrics in a course which taught from Studenmund but was extremely bored.

• econometrics
• reference-request

• 1 $\begingroup$ Are there particular areas that interest you, like causal inference, structural, time-series? $\endgroup$ –  dimitriy Commented Dec 5, 2014 at 22:30
• $\begingroup$ @DimitriyV.Masterov: I don't claim to know a ton about econometrics, but I definitely would be interested in casual inference and time-series. $\endgroup$ –  Clarinetist Commented Dec 5, 2014 at 22:31
• $\begingroup$ Can we again have one main reference per answer? That way people could vote single books. Was a suggestion in the other question and I feel it worked great. $\endgroup$ –  FooBar Commented Dec 5, 2014 at 23:09
• 1 $\begingroup$ An excellent treatment (published 1991) you may want to look into hup.harvard.edu/catalog.php?isbn=9780674175440 . I would also second: amazon.com/Primer-Econometric-Theory-MIT-Press/dp/0262034905 $\endgroup$ –  PatrickT Commented Mar 13, 2018 at 11:39

4 Answers 4

"Adult" Wooldridge is great intro to various microeconometrics topics.

For time series, Hamilton's Time Series and Lutkepohl's Introduction to Multiple Time Series Analysis are both nice, though Hamilton is a bit dated and Lutkepohl is more focused.

As far as more foundational, rigorous material, Herman Bierens has a short Introduction to the Mathematical and Statistical Foundations of Econometrics. Gourieroux and Monfort have a whole flock of graduate econometrics texts, but to quote an anonymous reviewer, they are "in the French tradition of excellent precision and terrible pedagogics," though they have their champions.

• 1 $\begingroup$ The Wooldridge book that @Dimitriy is talking about is "Econometric Analysis of Cross Section and Panel Data." "Baby" Wooldridge is "Introductory Econometrics: A Modern Approach." $\endgroup$ –  jmbejara Commented Dec 5, 2014 at 23:02
• 1 $\begingroup$ (+1) for also relaying the comment on Gourieroux and Monfort. $\endgroup$ –  Alecos Papadopoulos Commented Dec 5, 2014 at 23:06
• $\begingroup$ Thank you for telling me about Wooldridge. I have really enjoyed the text and it is right up my alley. :) $\endgroup$ –  Clarinetist Commented Jan 24, 2015 at 20:22

I repeat part of my answer in the question the OP already mentioned with some additional proposals:

Since you have a background in Statistics "Probability Theory and Statistical Inference: Econometric Modeling with Observational Data" 1999, by A. Spanos, provides the statistical foundations of econometrics in a way no other book does.

"Econometrics" by Hayasi , because, except of presenting a new synthesis focused around Extremum Estimators (i.e. Maximum Likelihood and Generalized Method of Moments), and of giving space to Time Series, Unit root econometrics and Co-integration, it has theoretical depth alongside very practical applications, a combination which is not usually found.

For Time-series, I don't believe you can ignore

Hamilton's Time Series Analysis" ,

" Lutkepohl's "Introduction to Multiple Time Series" which has gone into its 2nd ed. as "New Introduction to Multiple Time Series" (my experience is from the first edition).

• $\begingroup$ A book's "theoretical depth" is questionable when "measurability" appears exactly once in the book and is in quotes. $\endgroup$ –  Michael Commented Dec 6, 2014 at 14:41
• 3 $\begingroup$ @Michael And since when the concept of "measurability" and related, became the standard to measure theoretical depth? $\endgroup$ –  Alecos Papadopoulos Commented Dec 6, 2014 at 15:01

Can't forget "Econometric Analysis" by William Greene . It's in its 7th edition . It seems to get referenced lot. It sometimes skips over some detail but it does cover a broad range of topics. (Though I do like the pedagogical approach of something like Hayashi better, I think Greene might cover more ground.)

I am tempted to deviate from other answers and suggest that the best Econometrics textbook for someone with a strong enough mathematical background might not be a "mathematically-minded" econometrics textbook, but rather a textbook that focuses on "empirical methods and economics research", the best of which at the moment seems to remain:

Mostly Harmless Econometrics ( https://www.mostlyharmlesseconometrics.com/ )

Since you already have a strong mathematical background --- and unless you're specifically interested in the mathematical aspects of econometrics --- you are likely to learn much more (and find much more inspiration) from a book that starts and ends with a strong focus on causal inference and research design.

(This being said, by all means, do consult other answers in particular if you want to feed your interest in time-series analysis which is not covered much --- if at all --- in Mostly Harmless)

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    phdeconomics.com        a site for past, current, and future economics ph.d. students

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Graduate Economics Books

This page provides a listing, broken down by field, of the most popular graduate-level economics books.

Microeconomics

• Microeconomic Theory (Andreu Mas-Colell, Michael D. Whinston, Jerry R. Green)
• Microeconomic Analysis (Hal R. Varian)
• Advanced Microeconomic Theory (Geoffrey A. Jehle, Philip J. Reny)
• A Course in Microeconomic Theory (David M. Kreps)
• Microeconomic Foundations I: Choice and Competitive Markets (David M. Kreps)

Macroeconomics

• Recursive Macroeconomic Theory (Lars Ljungqvist, Thomas J. Sargent)
• Dynamic Economics: Quantitative Methods and Applications (Jerome Adda, Russell W. Cooper), Kindle edition
• Advanced Macroeconomics (David Romer), Kindle edition
• Introduction to Modern Economic Growth (Daron Acemoglu), Kindle edition
• Economic Growth (Robert J. Barro, Xavier Sala-i-Martin), Kindle edition
• Recursive Methods in Economic Dynamics (Nancy L. Stokey, Robert E. Lucas Jr.) ( solutions manual )
• Lectures on Macroeconomics (Olivier Jean Blanchard, Stanley Fischer)

Econometrics

• Mostly Harmless Econometrics (Joshua D. Angrist, Jorn-Steffen Pischke), Kindle edition
• A Guide to Econometrics (Peter Kennedy)
• Econometric Analysis of Cross Section and Panel Data (Jeffrey M. Wooldridge) ( solutions manual ), Kindle edition
• Econometrics (Fumio Hayashi), Kindle edition
• Microeconometrics: Methods and Applications (A. Colin Cameron, Pravin K. Trivedi)
• Time Series Analysis (James D. Hamilton)
• Econometric Analysis (William H. Greene), Kindle edition
• Statistical Inference (George Casella, Roger L. Berger)

International Economics

• Foundations of International Macroeconomics (Maurice Obstfeld, Kenneth S. Rogoff)
• Advanced International Trade: Theory and Evidence (Robert C. Feenstra), Kindle edition

Game Theory, Auction Theory, Industrial Organization

• Social and Economic Networks (Matthew O. Jackson), Kindle edition
• Game Theory for Applied Economists (Robert Gibbons), Kindle edition
• Contract Theory (Patrick Bolton, Mathias Dewatripont) ( solutions manual ), Kindle edition
• Game Theory: Analysis of Conflict (Roger B. Myerson)
• Game Theory (Drew Fudenberg, Jean Tirole)
• Auction Theory (Vijay Krishna), Kindle edition
• A Course in Game Theory (Martin J. Osborne, Ariel Rubinstein)
• The Theory of Industrial Organization (Jean Tirole), Kindle edition

Mathematical and Numerical Methods

• Mathematics for Economists (Carl P. Simon, Lawrence E. Blume)
• A First Course in Optimization Theory (Rangarajan K. Sundaram)
• Numerical Methods in Economics (Kenneth L. Judd)
• An Introduction to Mathematical Analysis for Economic Theory and Econometrics (Dean Corbae, Maxwell B. Stinchcombe, Juraj Zeman), Kindle edition
• Infinite Dimensional Analysis: A Hitchhiker's Guide (Charalambos D. Aliprantis, Kim C. Border)
• Fundamental Methods of Mathematical Economics (Kevin Wainwright, Alpha Chiang)

 phdeconomics.com 2009-2011

D. Zack Garza

He/Him/His Mathematics, University of Georgia Office: 438 Boyd [email protected]

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Recommendations: Graduate Level Texts and Notes

5 minute read

Inspired by the following Twitter thread:

Yo math tweeps, what is an absolutely standard textbook in your field that'd be accessible to advanced undergrads and/or newbie grad students? — D. Zack Garza (∂² ⋘ 0) (@dzackgarza) October 11, 2020

Multiple Areas

The Chicago Undergraduate Mathematics Bibliography

Comments on a number of mathematics books

Milne’s Collection of Course Notes

Pete Clark’s Expositions

PDF Notes for a variety of Cambridge courses

Qiaochu Yuan has some reading recommendations

This blog also has a large list of recommendations, sorted by topic

As does this one

There is a similar community wiki on MSE

You can find ranked recommendations on this question on MO

UC Berkeley has a bibliography of books used by class.

Real Analysis

Gerald b. folland, real analysis: modern techniques and applications.

• Links to some homeworks and solutions at UCSD

Walter Rudin, Real and Complex Analysis

• Useful as a general reference, but there are more useful techniques in other books.

Stein and Shakarchi, Real Analysis: Measure Theory, Integration, and Hilbert Spaces

• Has more useful techniques than Rudin.
• Doesn’t include $L^p$ spaces or convexity.

Lieb-Loss, Real Analysis

• Good source for $L^p$ spaces, convexity, and Fourier analysis.

Stein and Shakarchi, Fourier Analysis

• Very elementary.

Schilling, Measures, Integrals, and Martingales

Royden, real analysis, complex analysis, taylor, complex analysis, simon, complex analysis, stein and shakarchi, complex analysis, lars ahlfors, complex analysis, conway, functions of one complex variable i, functional analysis, conway, a course in functional analysis, differential equations, evans, partial differential equations.

• Good source for Sobolev spaces.

V. Arnold, Ordinary Differential Equations

Probability, s. ross, a first course in probability (prentice-hall), shiryayev, probability., feller, an introduction to probability theory and its applications, durrett, probability: theory and examples, general/introductory, dummit and foote, abstract algebra.

• Standard reference, encyclopaedic!

Hungerford, Algebra

Isaacs, algebra, m. artin, algebra, commutative algebra, altman-kleiman, a term of commutative algebra.

https://www.mi.fu-berlin.de/en/math/groups/arithmetic_geometry/teaching/exercises/Altman_-Kleiman---A-term-of-commutative-algebra-_2017_.pdf

Atiyah and MacDonald, Introduction to Commutative Algebra

Representation theory.

• Gaitsgory, Course Notes on Geometric Representation Theory
• Mcgerty, Notes on Lie Groups/Algebras
• From a physicist’s perspective

J-P. Serre, Linear Representations of Finite Groups

Humphreys, introduction to lie algebras and representation theory, pramod achar, unreleased geometric representation theory text, nicolas libedinsky, gentle introduction to soergel bimodules.

• Notes: Gentle introduction to Soergel bimodules I: The basics

Hall, Lie Groups, Lie Algebras, and Representations

Kirillov, introduction to lie groups and lie algebras.

• Recommended by Daniel Litt, link to notes

Homological

Peter j. hilton and urs stammbach, a course in homological algebra.

• Recommended by Dan Nakano

Kirillov, Lie Groups and Lie Algebras

• Recommended by Daniel Litt

Algebraic Geometry

• Gathmann, Notes
• Vakil, Rising Sea Notes

Robin Hartshorne, Algebraic Geometry

Eisenbud and harris, the geometry of schemes.

• Standard! Notes here: The Geometry of Schemes

Mumford, The Red Book of Varieties and Schemes

Number theory, algebraic number theory, j. neukirch, algebraic number theory.

• Large number of exercises here

Cassels and Fröhlich, Algebraic Number Theory

J. milne, algebraic number theory.

• Not a textbook: actually notes

Uncategorized

J.-p. serre, a course in arithmetic, silverman, the arithmetic of elliptic curves, marcus, number fields.

• Covers quadratic fields

J.-P. Serre, Local fields

F. lorenz, algebra volume ii: fields with structure, algebras and advanced topics, j. milne, class field theory, weil, basic number theory, saban alaca and kenneth williams, introductory algebraic number theory, valenza, fourier analysis on number fields.

• Slightly out-of-date

Algebraic Topology

Hatcher, algebraic topology.

• Standard reference.

Peter May, A Concise Course in Algebraic Topology

Dodson and parker, a user’s guide to algebraic topology.

• Covers more advanced topics than a usual course: some sheaf theory, bundles, characteristic classes, obstruction theory
• Appendices on algebra, topology, manifolds/bundles, and tables of homotopy groups

Glen Bredon, Topology and Geometry

• Blends differential and algebraic topology, can be disorienting as a first pass

Milnor, Topology from the Differentiable Viewpoint (Princeton)

• Classic reference.

Bott and Tu, Differential Forms in Algebraic Topology (Springer)

Massey, a basic course in algebraic topology, homotopy theory.

• Dwyer-Spalinski, Homotopy Theories and Model Categories
• Hovey-Shipley-Smith, Symmetric Spectra
• Hovey, Spectra and symmetric spectra in general model categories

Bott-Tu, Differential Forms in Algebraic Topology

Griffiths-morgan, rational homotopy theory and differential forms, mosher-tangora, cohomology operations and applications in homotopy theory, milnor, topology from the differentiable viewpoint., differential geometry and topology, manfredo p. do carmo, riemannian geometry, manfredo p. do carmo, differential geometry of curves and surfaces, guillemin and pollack, differential topology, john m. lee, introduction to smooth manifolds, milnor, morse theory, pollack, differential topology, milnor, lectures on h-cobordism, frank warner, foundations of differentiable manifolds and lie groups (), guillemin, stable mappings and their singularities, symplectic geometry/topology, dusa mcduff, introduction to symplectic topology, eliashberg, from stein to weinstein and back, cannas da silva, lectures on symplectic geometry.

• Skip chapters 4, 5, 25, 26, 30

Complex Geometry

Claire voisin, hodge theory and complex algebraic geometry, volumes i and ii, daniel huybrechts, complex geometry an introduction, griffiths-harris, principles of algebraic geometry, carlson, period mappings and period domains, rick miranda, algebraic curves and riemann surfaces, f. kirwan, complex algebraic curves, voison, hodge theory and complex algebraic geometry i.

• Notes: Period Mappings and Period Domains

Knot Theory

• Justin Roberts, Knots Knotes

Rolfsen, Knots and Links

Livingston, knot theory, colin adams, the knot book, turner, five lectures of khovanov homology.

• Arxiv, link

Bar-Natan, On Khovanov’s categorification of the Jones polynomial

J. kock, frobenius algebras and 2d tqfts, osvath and szabo, grid homology for knots and links, geometric group theory.

Here are 4 books that all my grad students read: 1. Brown's "Cohomology of groups" 2. Serre's "Trees" (followed up w/ Scott's article "Topological methods in group theory") 3. Witte-Morris's "Introduction to arithmetic groups" 4. Farb-Margalit's "Primer on mapping class groups" — Andrew Putman (@AndyPutmanMath) October 11, 2020

Serre, Trees

• Recommendation from Andrew Putman: follow up with Scott’s Topological Methods in Group Theory

Witte-Morris, Introduction to Arithmetic Groups

Farb-margalit, primer on mapping class groups, mark srednicki, quantum field theory, pierre deligne, quantum fields and strings: a course for mathematicians, howard georgi, lie algebras in particle physics, kusse, mathematical physics, combinatorics, stanley, enumerative combinatorics vol 1, bruce sagan - springer, the symmetric group, doug west, introduction to graph theory, kunen, set theory: an introduction to independence proofs, model theory, tent and ziegler, a course in model theory, unsorted recommendations, sipser, introduction to the theory of computation, murray, mathematical biology, mac lane-moerdijk, sheaves in geometry and logic: a first introduction to topos theory, you may also enjoy.

Some notes on Krantz’s “A Mathematician’s survival guide”

17 minute read

Recommendations: Undergraduate Resources

10 minute read

What To Do as an Undergraduate

Intro to Derived Algebraic Geometry 1: The Cotangent Complex and Derived de Rham Cohomology

Some introductory notes on derived algebraic geometry from an MSRI workshop series.

Introduction to Infinity Categories

Some notes on a short introductory video on some foundational aspects of infinity categories.

Econometrics

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Economics & Finance

The most authoritative and up-to-date core econometrics textbook available

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Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics.

• Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgrounds
• Draws on integrated, research-level datasets, provided on an accompanying website
• Discusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learning
• Features hundreds of exercises that enable students to learn by doing
• Includes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examples
• Can serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen’s Probability and Statistics for Economists

• Acknowledgments
• 1.1 What Is Econometrics?
• 1.2 The Probability Approach to Econometrics
• 1.3 Econometric Terms
• 1.4 Observational Data
• 1.5 Standard Data Structures
• 1.6 Econometric Software
• 1.7 Replication
• 1.8 Data Files for Textbook
• 1.9 Reading the Book
• 2.1 Introduction
• 2.2 The Distribution of Wages
• 2.3 Conditional Expectation
• 2.4 Logs and Percentages
• 2.5 Conditional Expectation Function
• 2.6 Continuous Variables
• 2.7 Law of Iterated Expectations
• 2.8 CEF Error
• 2.9 Intercept-Only Model
• 2.10 Regression Variance
• 2.11 Best Predictor
• 2.12 Conditional Variance
• 2.13 Homoskedasticity and Heteroskedasticity
• 2.14 Regression Derivative
• 2.15 Linear CEF
• 2.16 Linear CEF with Nonlinear Effects
• 2.17 Linear CEF with Dummy Variables
• 2.18 Best Linear Predictor
• 2.19 Illustrations of Best Linear Predictor
• 2.20 Linear Predictor Error Variance
• 2.21 Regression Coefficients
• 2.22 Regression Subvectors
• 2.23 Coefficient Decomposition
• 2.24 Omitted Variable Bias
• 2.25 Best Linear Approximation
• 2.26 Regression to the Mean
• 2.27 Reverse Regression
• 2.28 Limitations of the Best Linear Projection
• 2.29 Random Coefficient Model
• 2.30 Causal Effects
• 2.31 Existence and Uniqueness of the Conditional Expectation*
• 2.32 Identification*
• 2.33 Technical Proofs*
• 2.34 Exercises
• 3.1 Introduction
• 3.2 Samples
• 3.3 Moment Estimators
• 3.4 Least Squares Estimator
• 3.5 Solving for Least Squares with One Regressor
• 3.6 Solving for Least Squares with Multiple Regressors
• 3.7 Illustration
• 3.8 Least Squares Residuals
• 3.9 Demeaned Regressors
• 3.10 Model in Matrix Notation
• 3.11 Projection Matrix
• 3.12 Annihilator Matrix
• 3.13 Estimation of Error Variance
• 3.14 Analysis of Variance
• 3.15 Projections
• 3.16 Regression Components
• 3.17 Regression Components (Alternative Derivation)*
• 3.18 Residual Regression
• 3.19 Leverage Values
• 3.20 Leave-One-Out Regression
• 3.21 Influential Observations
• 3.22 CPS Dataset
• 3.23 Numerical Computation
• 3.24 Collinearity Errors
• 3.25 Programming
• 3.26 Exercises
• 4.1 Introduction
• 4.2 Random Sampling
• 4.3 Sample Mean
• 4.4 Linear Regression Model
• 4.5 Expectation of Least Squares Estimator
• 4.6 Variance of Least Squares Estimator
• 4.7 Unconditional Moments
• 4.8 Gauss-Markov Theorem
• 4.9 Generalized Least Squares
• 4.10 Residuals
• 4.11 Estimation of Error Variance
• 4.12 Mean-Squared Forecast Error
• 4.13 Covariance Matrix Estimation under Homoskedasticity
• 4.14 Covariance Matrix Estimation under Heteroskedasticity
• 4.15 Standard Errors
• 4.16 Estimation with Sparse Dummy Variables
• 4.17 Computation
• 4.18 Measures of Fit
• 4.19 Empirical Example
• 4.20 Multicollinearity
• 4.21 Clustered Sampling
• 4.22 Inference with Clustered Samples
• 4.23 At What Level to Cluster?
• 4.24 Technical Proofs*
• 4.25 Exercises
• 5.1 Introduction
• 5.2 The Normal Distribution
• 5.3 Multivariate Normal Distribution
• 5.4 Joint Normality and Linear Regression
• 5.5 Normal Regression Model
• 5.6 Distribution of OLS Coefficient Vector
• 5.7 Distribution of OLS Residual Vector
• 5.8 Distribution of Variance Estimator
• 5.9 t-Statistic
• 5.10 Confidence Intervals for Regression Coefficients
• 5.11 Confidence Intervals for Error Variance
• 5.12 t-Test
• 5.13 Likelihood Ratio Test
• 5.14 Information Bound for Normal Regression
• 5.15 Exercises
• 6.1 Introduction
• 6.2 Modes of Convergence
• 6.3 Weak Law of Large Numbers
• 6.4 Central Limit Theorem
• 6.5 Continuous Mapping Theorem and Delta Method
• 6.6 Smooth Function Model
• 6.7 Stochastic Order Symbols
• 6.8 Convergence of Moments
• 7.1 Introduction
• 7.2 Consistency of Least Squares Estimator
• 7.3 Asymptotic Normality
• 7.4 Joint Distribution
• 7.5 Consistency of Error Variance Estimators
• 7.6 Homoskedastic Covariance Matrix Estimation
• 7.7 Heteroskedastic Covariance Matrix Estimation
• 7.8 Summary of Covariance Matrix Notation
• 7.9 Alternative Covariance Matrix Estimators*
• 7.10 Functions of Parameters
• 7.11 Asymptotic Standard Errors
• 7.12 t-Statistic
• 7.13 Confidence Intervals
• 7.14 Regression Intervals
• 7.15 Forecast Intervals
• 7.16 Wald Statistic
• 7.17 Homoskedastic Wald Statistic
• 7.18 Confidence Regions
• 7.19 Edgeworth Expansion*
• 7.20 Uniformly Consistent Residuals*
• 7.21 Asymptotic Leverage*
• 7.22 Exercises
• 8.1 Introduction
• 8.2 Constrained Least Squares
• 8.3 Exclusion Restriction
• 8.4 Finite Sample Properties
• 8.5 Minimum Distance
• 8.6 Asymptotic Distribution
• 8.7 Variance Estimation and Standard Errors
• 8.8 Efficient Minimum Distance Estimator
• 8.9 Exclusion Restriction Revisited
• 8.10 Variance and Standard Error Estimation
• 8.11 Hausman Equality
• 8.12 Example: Mankiw, Romer, and Weil (1992)
• 8.13 Misspecification
• 8.14 Nonlinear Constraints
• 8.15 Inequality Restrictions
• 8.16 Technical Proofs*
• 8.17 Exercises
• 9.1 Introduction
• 9.2 Hypotheses
• 9.3 Acceptance and Rejection
• 9.4 Type I Error
• 9.5 T-Tests
• 9.6 Type II Error and Power
• 9.7 Statistical Significance
• 9.8 p-Values
• 9.9 t-Ratios and the Abuse of Testing
• 9.10 Wald Tests
• 9.11 Homoskedastic Wald Tests
• 9.12 Criterion-Based Tests
• 9.13 Minimum Distance Tests
• 9.14 Minimum Distance Tests under Homoskedasticity
• 9.15 F Tests
• 9.16 Hausman Tests
• 9.17 Score Tests
• 9.18 Problems with Tests of Nonlinear Hypotheses
• 9.19 Monte Carlo Simulation
• 9.20 Confidence Intervals by Test Inversion
• 9.21 Multiple Tests and Bonferroni Corrections
• 9.22 Power and Test Consistency
• 9.23 Asymptotic Local Power
• 9.24 Asymptotic Local Power, Vector Case
• 9.25 Exercises
• 10.1 Introduction
• 10.2 Example
• 10.3 Jackknife Estimation of Variance
• 10.4 Example
• 10.5 Jackknife for Clustered Observations
• 10.6 The Bootstrap Algorithm
• 10.7 Bootstrap Variance and Standard Errors
• 10.8 Percentile Interval
• 10.9 The Bootstrap Distribution
• 10.10 The Distribution of the Bootstrap Observations
• 10.11 The Distribution of the Bootstrap Sample Mean
• 10.12 Bootstrap Asymptotics
• 10.13 Consistency of the Bootstrap Estimate of Variance
• 10.14 Trimmed Estimator of Bootstrap Variance
• 10.15 Unreliability of Untrimmed Bootstrap Standard Errors
• 10.16 Consistency of the Percentile Interval
• 10.17 Bias-Corrected Percentile Interval
• 10.18 BCa Percentile Interval
• 10.19 Percentile-t Interval
• 10.20 Percentile-t Asymptotic Refinement
• 10.21 Bootstrap Hypothesis Tests
• 10.22 Wald-Type Bootstrap Tests
• 10.23 Criterion-Based Bootstrap Tests
• 10.24 Parametric Bootstrap
• 10.25 How Many Bootstrap Replications?
• 10.26 Setting the Bootstrap Seed
• 10.27 Bootstrap Regression
• 10.28 Bootstrap Regression Asymptotic Theory
• 10.29 Wild Bootstrap
• 10.30 Bootstrap for Clustered Observations
• 10.31 Technical Proofs*
• 10.32 Exercises
• 11.1 Introduction
• 11.2 Regression Systems
• 11.3 Least Squares Estimator
• 11.4 Expectation and Variance of Systems Least Squares
• 11.5 Asymptotic Distribution
• 11.6 Covariance Matrix Estimation
• 11.7 Seemingly Unrelated Regression
• 11.8 Equivalence of SUR and Least Squares
• 11.9 Maximum Likelihood Estimator
• 11.10 Restricted Estimation
• 11.11 Reduced Rank Regression
• 11.12 Principal Component Analysis
• 11.13 Factor Models
• 11.14 Approximate Factor Models
• 11.15 Factor Models with Additional Regressors
• 11.16 Factor-Augmented Regression
• 11.17 Multivariate Normal*
• 11.18 Exercises
• 12.1 Introduction
• 12.2 Overview
• 12.3 Examples
• 12.4 Endogenous Regressors
• 12.5 Instruments
• 12.6 Example: College Proximity
• 12.7 Reduced Form
• 12.8 Identification
• 12.9 Instrumental Variables Estimator
• 12.10 Demeaned Representation
• 12.11 Wald Estimator
• 12.12 Two-Stage Least Squares
• 12.13 Limited Information Maximum Likelihood
• 12.14 Split-Sample IV and JIVE
• 12.15 Consistency of 2SLS
• 12.16 Asymptotic Distribution of 2SLS
• 12.17 Determinants of 2SLS Variance
• 12.18 Covariance Matrix Estimation
• 12.19 LIML Asymptotic Distribution
• 12.20 Functions of Parameters
• 12.21 Hypothesis Tests
• 12.22 Finite Sample Theory
• 12.23 Bootstrap for 2SLS
• 12.24 The Peril of Bootstrap 2SLS Standard Errors
• 12.25 Clustered Dependence
• 12.26 Generated Regressors
• 12.27 Regression with Expectation Errors
• 12.28 Control Function Regression
• 12.29 Endogeneity Tests
• 12.30 Subset Endogeneity Tests
• 12.31 Overidentification Tests
• 12.32 Subset Overidentification Tests
• 12.33 Bootstrap Overidentification Tests
• 12.34 Local Average Treatment Effects
• 12.35 Identification Failure
• 12.36 Weak Instruments
• 12.37 Many Instruments
• 12.38 Testing for Weak Instruments
• 12.39 Weak Instruments with k2 > 1
• 12.40 Example: Acemoglu, Johnson, and Robinson (2001)
• 12.41 Example: Angrist and Krueger (1991)
• 12.42 Programming
• 12.43 Exercises
• 13.1 Introduction
• 13.2 Moment Equation Models
• 13.3 Method of Moments Estimators
• 13.4 Overidentified Moment Equations
• 13.5 Linear Moment Models
• 13.6 GMM Estimator
• 13.7 Distribution of GMM Estimator
• 13.8 Efficient GMM
• 13.9 Efficient GMM versus 2SLS
• 13.10 Estimation of the Efficient Weight Matrix
• 13.11 Iterated GMM
• 13.12 Covariance Matrix Estimation
• 13.13 Clustered Dependence
• 13.14 Wald Test
• 13.15 Restricted GMM
• 13.16 Nonlinear Restricted GMM
• 13.17 Constrained Regression
• 13.18 Multivariate Regression
• 13.19 Distance Test
• 13.20 Continuously Updated GMM
• 13.21 Overidentification Test
• 13.22 Subset Overidentification Tests
• 13.23 Endogeneity Test
• 13.24 Subset Endogeneity Test
• 13.25 Nonlinear GMM
• 13.26 Bootstrap for GMM
• 13.27 Conditional Moment Equation Models
• 13.28 Technical Proofs*
• 13.29 Exercises
• 14.1 Introduction
• 14.2 Examples
• 14.3 Differences and Growth Rates
• 14.4 Stationarity
• 14.5 Transformations of Stationary Processes
• 14.6 Convergent Series
• 14.7 Ergodicity
• 14.8 Ergodic Theorem
• 14.9 Conditioning on Information Sets
• 14.10 Martingale Difference Sequences
• 14.11 CLT for Martingale Differences
• 14.12 Mixing
• 14.13 CLT for Correlated Observations
• 14.14 Linear Projection
• 14.15 White Noise
• 14.16 The Wold Decomposition
• 14.17 Lag Operator
• 14.18 Autoregressive Wold Representation
• 14.19 Linear Models
• 14.20 Moving Average Process
• 14.21 Infinite-Order Moving Average Process
• 14.22 First-Order Autoregressive Process
• 14.23 Unit Root and Explosive AR(1) Processes
• 14.24 Second-Order Autoregressive Process
• 14.25 AR(p) Process
• 14.26 Impulse Response Function
• 14.27 ARMA and ARIMA Processes
• 14.28 Mixing Properties of Linear Processes
• 14.29 Identification
• 14.30 Estimation of Autoregressive Models
• 14.31 Asymptotic Distribution of Least Squares Estimator
• 14.32 Distribution under Homoskedasticity
• 14.33 Asymptotic Distribution under General Dependence
• 14.34 Covariance Matrix Estimation
• 14.35 Covariance Matrix Estimation under General Dependence
• 14.36 Testing the Hypothesis of No Serial Correlation
• 14.37 Testing for Omitted Serial Correlation
• 14.38 Model Selection
• 14.39 Illustrations
• 14.40 Time Series Regression Models
• 14.41 Static, Distributed Lag, and Autoregressive Distributed Lag Models
• 14.42 Time Trends
• 14.43 Illustration
• 14.44 Granger Causality
• 14.45 Testing for Serial Correlation in Regression Models
• 14.46 Bootstrap for Time Series
• 14.47 Technical Proofs*
• 14.48 Exercises
• 15.1 Introduction
• 15.2 Multiple Equation Time Series Models
• 15.3 Linear Projection
• 15.4 Multivariate Wold Decomposition
• 15.5 Impulse Response
• 15.6 VAR(1) Model
• 15.7 VAR(p) Model
• 15.8 Regression Notation
• 15.9 Estimation
• 15.10 Asymptotic Distribution
• 15.11 Covariance Matrix Estimation
• 15.12 Selection of Lag Length in a VAR
• 15.13 Illustration
• 15.14 Predictive Regressions
• 15.15 Impulse Response Estimation
• 15.16 Local Projection Estimator
• 15.17 Regression on Residuals
• 15.18 Orthogonalized Shocks
• 15.19 Orthogonalized Impulse Response Function
• 15.20 Orthogonalized Impulse Response Estimation
• 15.21 Illustration
• 15.22 Forecast Error Decomposition
• 15.23 Identification of Recursive VARs
• 15.24 Oil Price Shocks
• 15.25 Structural VARs
• 15.26 Identification of Structural VARs
• 15.27 Long-Run Restrictions
• 15.28 Blanchard and Quah (1989) Illustration
• 15.29 External Instruments
• 15.30 Dynamic Factor Models
• 15.31 Technical Proofs*
• 15.32 Exercises
• 16.1 Introduction
• 16.2 Partial Sum Process and Functional Convergence
• 16.3 Beveridge-Nelson Decomposition
• 16.4 Functional CLT
• 16.5 Orders of Integration
• 16.6 Means, Local Means, and Trends
• 16.7 Demeaning and Detrending
• 16.8 Stochastic Integrals
• 16.9 Estimation of an AR(1)
• 16.10 AR(1) Estimation with an Intercept
• 16.11 Sample Covariances of Integrated and Stationary Processes
• 16.12 AR(p) Models with a Unit Root
• 16.13 Testing for a Unit Root
• 16.14 KPSS Stationarity Test
• 16.15 Spurious Regression
• 16.16 Nonstationary VARs
• 16.17 Cointegration
• 16.18 Role of Intercept and Trend
• 16.19 Cointegrating Regression
• 16.20 VECM Estimation
• 16.21 Testing for Cointegration in a VECM
• 16.22 Technical Proofs*
• 16.23 Exercises
• 17.1 Introduction
• 17.2 Time Indexing and Unbalanced Panels
• 17.3 Notation
• 17.4 Pooled Regression
• 17.5 One-Way Error Component Model
• 17.6 Random Effects
• 17.7 Fixed Effects Model
• 17.8 Within Transformation
• 17.9 Fixed Effects Estimator
• 17.10 Differenced Estimator
• 17.11 Dummy Variables Regression
• 17.12 Fixed Effects Covariance Matrix Estimation
• 17.13 Fixed Effects Estimation in Stata
• 17.14 Between Estimator
• 17.15 Feasible GLS
• 17.16 Intercept in Fixed Effects Regression
• 17.17 Estimation of Fixed Effects
• 17.18 GMM Interpretation of Fixed Effects
• 17.19 Identification in the Fixed Effects Model
• 17.20 Asymptotic Distribution of Fixed Effects Estimator
• 17.21 Asymptotic Distribution for Unbalanced Panels
• 17.22 Heteroskedasticity-Robust Covariance Matrix Estimation
• 17.23 Heteroskedasticity-Robust Estimation—Unbalanced Case
• 17.24 Hausman Test for Random vs. Fixed Effects
• 17.25 Random Effects or Fixed Effects?
• 17.26 Time Trends
• 17.27 Two-Way Error Components
• 17.28 Instrumental Variables
• 17.29 Identification with Instrumental Variables
• 17.30 Asymptotic Distribution of Fixed Effects 2SLS Estimator
• 17.31 Linear GMM
• 17.32 Estimation with Time-Invariant Regressors
• 17.33 Hausman-Taylor Model
• 17.34 Jackknife Covariance Matrix Estimation
• 17.35 Panel Bootstrap
• 17.36 Dynamic Panel Models
• 17.37 The Bias of Fixed Effects Estimation
• 17.38 Anderson-Hsiao Estimator
• 17.39 Arellano-Bond Estimator
• 17.40 Weak Instruments
• 17.41 Dynamic Panels with Predetermined Regressors
• 17.42 Blundell-Bond Estimator
• 17.43 Forward Orthogonal Transformation
• 17.44 Empirical Illustration
• 17.45 Exercises
• 18.1 Introduction
• 18.2 Minimum Wage in New Jersey
• 18.3 Identification
• 18.4 Multiple Units
• 18.5 Do Police Reduce Crime?
• 18.6 Trend Specification
• 18.7 Do Blue Laws Affect Liquor Sales?
• 18.8 Check Your Code: Does Abortion Impact Crime?
• 18.9 Inference
• 18.10 Exercises
• 19.1 Introduction
• 19.2 Binned Means Estimator
• 19.3 Kernel Regression
• 19.4 Local Linear Estimator
• 19.5 Local Polynomial Estimator
• 19.6 Asymptotic Bias
• 19.7 Asymptotic Variance
• 19.9 Reference Bandwidth
• 19.10 Estimation at a Boundary
• 19.11 Nonparametric Residuals and Prediction Errors
• 19.12 Cross-Validation Bandwidth Selection
• 19.13 Asymptotic Distribution
• 19.14 Undersmoothing
• 19.15 Conditional Variance Estimation
• 19.16 Variance Estimation and Standard Errors
• 19.17 Confidence Bands
• 19.18 The Local Nature of Kernel Regression
• 19.19 Application to Wage Regression
• 19.20 Clustered Observations
• 19.21 Application to Test Scores
• 19.22 Multiple Regressors
• 19.23 Curse of Dimensionality
• 19.24 Partially Linear Regression
• 19.25 Computation
• 19.26 Technical Proofs*
• 19.27 Exercises
• 20.1 Introduction
• 20.2 Polynomial Regression
• 20.3 Illustrating Polynomial Regression
• 20.4 Orthogonal Polynomials
• 20.5 Splines
• 20.6 Illustrating Spline Regression
• 20.7 The Global/Local Nature of Series Regression
• 20.8 Stone-Weierstrass and Jackson Approximation Theory
• 20.9 Regressor Bounds
• 20.10 Matrix Convergence
• 20.11 Consistent Estimation
• 20.12 Convergence Rate
• 20.13 Asymptotic Normality
• 20.14 Regression Estimation
• 20.15 Undersmoothing
• 20.16 Residuals and Regression Fit
• 20.17 Cross-Validation Model Selection
• 20.18 Variance and Standard Error Estimation
• 20.19 Clustered Observations
• 20.20 Confidence Bands
• 20.21 Uniform Approximations
• 20.22 Partially Linear Model
• 20.23 Panel Fixed Effects
• 20.24 Multiple Regressors
• 20.25 Additively Separable Models
• 20.26 Nonparametric Instrumental Variables Regression
• 20.27 NPIV Identification
• 20.28 NPIV Convergence Rate
• 20.29 Nonparametric vs. Parametric Identification
• 20.30 Example: Angrist and Lavy (1999)
• 20.31 Technical Proofs*
• 20.32 Exercises
• 21.1 Introduction
• 21.2 Sharp Regression Discontinuity
• 21.3 Identification
• 21.4 Estimation
• 21.5 Inference
• 21.6 Bandwidth Selection
• 21.7 RDD with Covariates
• 21.8 A Simple RDD Estimator
• 21.9 Density Discontinuity Test
• 21.10 Fuzzy Regression Discontinuity
• 21.11 Estimation of FRD
• 21.12 Exercises
• 22.1 Introduction
• 22.2 Examples
• 22.3 Identification and Estimation
• 22.4 Consistency
• 22.5 Uniform Law of Large Numbers
• 22.6 Asymptotic Distribution
• 22.7 Asymptotic Distribution under Broader Conditions*
• 22.8 Covariance Matrix Estimation
• 22.9 Technical Proofs*
• 22.10 Exercises
• 23.1 Introduction
• 23.2 Identification
• 23.3 Estimation
• 23.4 Asymptotic Distribution
• 23.5 Covariance Matrix Estimation
• 23.6 Panel Data
• 23.7 Threshold Models
• 23.8 Testing for Nonlinear Components
• 23.9 Computation
• 23.10 Technical Proofs*
• 23.11 Exercises
• 24.1 Introduction
• 24.2 Median Regression
• 24.3 Least Absolute Deviations
• 24.4 Quantile Regression
• 24.5 Example Quantile Shapes
• 24.6 Estimation
• 24.7 Asymptotic Distribution
• 24.8 Covariance Matrix Estimation
• 24.9 Clustered Dependence
• 24.10 Quantile Crossings
• 24.11 Quantile Causal Effects
• 24.12 Random Coefficient Representation
• 24.13 Nonparametric Quantile Regression
• 24.14 Panel Data
• 24.15 IV Quantile Regression
• 24.16 Technical Proofs*
• 24.17 Exercises
• 25.1 Introduction
• 25.2 Binary Choice Models
• 25.3 Models for the Response Probability
• 25.4 Latent Variable Interpretation
• 25.5 Likelihood
• 25.6 Pseudo-True Values
• 25.7 Asymptotic Distribution
• 25.8 Covariance Matrix Estimation
• 25.9 Marginal Effects
• 25.10 Application
• 25.11 Semiparametric Binary Choice
• 25.12 IV Probit
• 25.13 Binary Panel Data
• 25.14 Technical Proofs*
• 25.15 Exercises
• 26.1 Introduction
• 26.2 Multinomial Response
• 26.3 Multinomial Logit
• 26.4 Conditional Logit
• 26.5 Independence of Irrelevant Alternatives
• 26.6 Nested Logit
• 26.7 Mixed Logit
• 26.8 Simple Multinomial Probit
• 26.9 General Multinomial Probit
• 26.10 Ordered Response
• 26.11 Count Data
• 26.12 BLP Demand Model
• 26.13 Technical Proofs*
• 26.14 Exercises
• 27.1 Introduction
• 27.2 Censoring
• 27.3 Censored Regression Functions
• 27.4 The Bias of Least Squares Estimation
• 27.5 Tobit Estimator
• 27.6 Identification in Tobit Regression
• 27.7 CLAD and CQR Estimators
• 27.8 Illustrating Censored Regression
• 27.9 Sample Selection Bias
• 27.10 Heckman’s Model
• 27.11 Nonparametric Selection
• 27.12 Panel Data
• 27.13 Exercises
• 28.1 Introduction
• 28.2 Model Selection
• 28.3 Bayesian Information Criterion
• 28.4 Akaike Information Criterion for Regression
• 28.5 Akaike Information Criterion for Likelihood
• 28.6 Mallows Criterion
• 28.7 Hold-Out Criterion
• 28.8 Cross-Validation Criterion
• 28.9 K-Fold Cross-Validation
• 28.10 Many Selection Criteria Are Similar
• 28.11 Relation with Likelihood Ratio Testing
• 28.12 Consistent Selection
• 28.13 Asymptotic Selection Optimality
• 28.14 Focused Information Criterion
• 28.15 Best Subset and Stepwise Regression
• 28.16 The MSE of Model Selection Estimators
• 28.17 Inference after Model Selection
• 28.18 Empirical Illustration
• 28.19 Shrinkage Methods
• 28.20 James-Stein Shrinkage Estimator
• 28.21 Interpretation of the Stein Effect
• 28.22 Positive Part Estimator
• 28.23 Shrinkage Toward Restrictions
• 28.24 Group James-Stein
• 28.25 Empirical Illustrations
• 28.26 Model Averaging
• 28.27 Smoothed BIC and AIC
• 28.28 Mallows Model Averaging
• 28.29 Jackknife (CV) Model Averaging
• 28.30 Granger-Ramanathan Averaging
• 28.31 Empirical Illustration
• 28.32 Technical Proofs*
• 28.33 Exercises
• 29.1 Introduction
• 29.2 Big Data, High Dimensionality, and Machine Learning
• 29.3 High-Dimensional Regression
• 29.4 p-norms
• 29.5 Ridge Regression
• 29.6 Statistical Properties of Ridge Regression
• 29.7 Illustrating Ridge Regression
• 29.9 Lasso Penalty Selection
• 29.10 Lasso Computation
• 29.11 Asymptotic Theory for the Lasso
• 29.12 Approximate Sparsity
• 29.13 Elastic Net
• 29.14 Post-Lasso
• 29.15 Regression Trees
• 29.16 Bagging
• 29.17 Random Forests
• 29.18 Ensembling
• 29.19 Lasso IV
• 29.20 Double Selection Lasso
• 29.21 Post-Regularization Lasso
• 29.22 Double/Debiased Machine Learning
• 29.23 Technical Proofs*
• 29.24 Exercises
• A.1 Notation
• A.2 Complex Matrices
• A.3 Matrix Addition
• A.4 Matrix Multiplication
• A.6 Rank and Inverse
• A.7 Orthogonal and Orthonormal Matrices
• A.8 Determinant
• A.9 Eigenvalues
• A.10 Positive Definite Matrices
• A.11 Idempotent Matrices
• A.12 Singular Values
• A.13 Matrix Decompositions
• A.14 Generalized Eigenvalues
• A.15 Extrema of Quadratic Forms
• A.16 Cholesky Decomposition
• A.17 QR Decomposition
• A.18 Solving Linear Systems
• A.19 Algorithmic Matrix Inversion
• A.20 Matrix Calculus
• A.21 Kronecker Products and the Vec Operator
• A.22 Vector Norms
• A.23 Matrix Norms
• B.1-Inequalities for Real Numbers
• B.2-Inequalities for Vectors
• B.3-Inequalities for Matrices
• B.4-Probability Inequalities
• B.5-Proofs*

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Books to Study Before Going to Graduate School in Economics

Must Read Books for Pre-Ph.D Economics Students

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• U.S. Economy
• Supply & Demand
• Archaeology
• Ph.D., Business Administration, Richard Ivey School of Business
• M.A., Economics, University of Rochester
• B.A., Economics and Political Science, University of Western Ontario

Q:  If I want to achieve a Ph.D. in economics what steps would you advise me to take and what books and courses would I need to study to gain the knowledge that is absolutely needed to be able to do and understand the research that is needed for a Ph.D.

A:  Thank you for your question. It's a question that I'm frequently asked, so it's about time that I created a page that I could point people toward.

It's really difficult to give you a general answer, because a lot of it depends on where you'd like to get your Ph.D. from. Ph.D programs in economics vary widely in both quality and scope of what is taught. The approach taken by European schools tends to be different than that of Canadian and American schools. The advice in this article will mainly apply to those who are interested in entering a Ph.D. program in the United States or Canada, but much of the advice should also apply to European programs as well. There are four key subject areas that you'll need to be very familiar with to succeed in a Ph.D. program in economics .

1. Microeconomics / Economic Theory

Even if you plan to study a subject which is closer to Macroeconomics or Econometrics , it is important to have a good grounding in Microeconomic Theory . A lot of work in subjects such as Political Economy and Public Finance are rooted in "micro foundations" so you'll help yourself immensely in these courses if you're already familiar with high level microeconomics. Most schools also require you to take at least two courses in microeconomics, and often these courses are the most difficult you'll encounter as a graduate student.

Microeconomics Material You Must Know as a Bare Minimum

I would recommend reviewing the book Intermediate Microeconomics: A Modern Approach by Hal R. Varian. The newest edition is the sixth one, bu if you can find an older used edition costing less you may want to do that.

Advanced Microeconomics Material that Would be Helpful to Know

Hal Varian has a more advanced book called simply Microeconomic Analysis . Most economics students are familiar with both books and refer to this book as simply "Varian" and the Intermediate book as "Baby Varian". A lot of the material in here is stuff you wouldn't be expected to know entering a program as it's often taught for the first time in Masters and Ph.D. programs. The more you can learn before you enter the Ph.D. program, the better you will do.

What Microeconomics Book You'll Use When You Get There

From what I can tell, Microeconomic Theory by Mas-Colell, Whinston, and Green is standard in many Ph.D. programs. It's what I used when I took Ph.D. courses in Microeconomics at both Queen's University at Kingston and the University of Rochester. It's an absolutely massive book, with hundreds and hundreds of practice questions. The book is quite difficult in parts so you'll want to have a good background in microeconomic theory before you tackle this one.

2. Macroeconomics

Giving advice on Macroeconomics books is a lot more difficult because Macroeconomics is taught so differently from school to school. Your best bet is to see what books are used in the school that you would like to attend. The books will be completely different depending on whether your school teaches more Keynesian style Macroeconomics or "Freshwater Macro" which is taught at places like "The Five Good Guys" which includes the University of Chicago, the University of Minnesota, Northwestern University, University of Rochester, and University of Pennsylvania.

The advice I'm going to give is for students who are going to a school that teaches more of a "Chicago" style approach.

Macroeconomics Material You Must Know as a Bare Minimum

I would recommend reviewing the book Advanced Macroeconomics by David Romer. Although it does have the word "Advanced" in the title, it's more suited for high level undergraduate study. It does have some Keynesian material as well. If you understand the material in this book, you should do well as a graduate student in Macroeconomics.

Advanced Macroeconomics Material that would be Helpful to Know

Instead of learning more Macroeconomics, it would be more helpful to learn more on dynamic optimization. See my section on Math Economics books for more detail.

What Macroeconomics Book You'll Use When You Get There

When I took Ph.D courses in Macroeconomics a few years ago we didn't really use any textbooks, instead we discussed journal articles. This is the case in most courses at the Ph.D. level. I was fortunate enough to have macroeconomics courses taught by Per Krusell and Jeremy Greenwood and you could spend an entire course or two just studying their work. One book that is used quite often is Recursive Methods in Economic Dynamics by Nancy L. Stokey and Robert E. Lucas Jr. Although the book is almost 15 years old, it's still quite useful for understanding the methodology behind many macroeconomics articles. I've also found Numerical Methods in Economics by Kenneth L. Judd to be quite helpful when you're trying to obtain estimates from a model which does not have a closed-form solution.

3. Econometrics Material You Must Know as a Bare Minimum

There's quite a few good undergraduate texts on Econometrics out there. When I taught tutorials in undergraduate Econometrics last year, we used Essentials of Econometrics by Damodar N. Gujarati. It's as useful as any other undergraduate text I've seen on Econometrics. You can usually pick up a good Econometrics text for very little money at a large second-hand book shop. A lot of undergraduate students can't seem to wait to discard their old econometrics materials.

Advanced Econometrics Material that would be Helpful to Know

I've found two books rather useful: Econometrics Analysis by William H. Greene and A Course in Econometrics by Arthur S. Goldberger. As in the Microeconomics section, these books cover a lot of material which is introduced for the first time at the graduate level. The more you know going in, though, the better chance you'll have of succeeding.

What Econometrics Book You'll Use When You Get There

Chances are you'll encounter the king of all Econometrics books Estimation and Inference in Econometrics by Russell Davidson and James G. MacKinnon. This is a terrific text, because it explains why things work like they do, and does not treat the matter as a "black box" like many econometrics books do. The book is quite advanced, though the material can be picked up fairly quickly if you have a basic knowledge of geometry.

4. Mathematics

Having a good understanding of mathematics is crucial to success in economics. Most undergraduate students, particularly those coming from North America, are often shocked by how mathematical graduate programs in economics are. The math goes beyond basic algebra and calculus, as it tends to be more proofs, such as "Let (x_n) be a Cauchy sequence. Show that if (X_n) has a convergent subsequence then the sequence is itself convergent". I've found that the most successful students in the first year of a Ph.D. program tend to be ones with mathematics backgrounds, not economics ones. That being said, there's no reason why someone with an economics background can not succeed.

Mathematical Economics Material You Must Know as a Bare Minimum

You'll certainly want to read a good undergraduate "Mathematics for Economists" type book. The best one that I've seen happens to be called Mathematics for Economists written by Carl P. Simon and Lawrence Blume. It has a quite diverse set of topics, all of which are useful tools for economic analysis.

If you're rusty on basic calculus, make sure you pick up a 1st year undergraduate calculus book. There are hundreds and hundreds of different ones available, so I'd suggest looking for one in a second hand shop. You may also want to review a good higher level calculus book such as Multivariable Calculus by James Stewart.

You should have at least a basic knowledge of differential equations, but you do not have to be an expert in them by any means. Reviewing the first few chapters of a book such as Elementary Differential Equations and Boundary Value Problems by William E. Boyce and Richard C. DiPrima would be quite useful. You do not need to have any knowledge of partial differential equations before entering graduate school, as they are generally only used in very specialized models.

If you're uncomfortable with proofs, you may want to pick up The Art and Craft of Problem Solving by Paul Zeitz. The material in the book has almost nothing to do with economics, but it will help you greatly when working on proofs. As an added bonus a lot of the problems in the book are surprisingly fun.

The more knowledge you have of pure mathematics subjects such as Real Analysis and Topology, the better. I would recommend working on as much of Introduction to Analysis by Maxwell Rosenlicht as you possibly can. The book costs less than $10 US but it is worth its weight in gold. There are other analysis books that are slightly better, but you cannot beat the price. You may also want to look at the Schaum's Outlines - Topology and Schaum's Outlines - Real Analysis . They're also quite inexpensive and have hundreds of useful problems. Complex analysis, while quite an interesting subject, will be of little use to a graduate student in economics, so you need not worry about it.

Advanced Mathematical Economics that would be Helpful to Know

The more real analysis you know, the better you will do. You may want to see one of the more canonical texts such as The Elements of Real Analysis by Robert G. Bartle. You may also want to look at the book I recommend in the next paragraph.

What Advanced Mathematical Economics Book You'll Use When You Get There

At the University of Rochester we used a book called A First Course in Optimization Theory by Rangarajan K. Sundaram, though I don't know how widely this is used. If you have a good understanding of real analysis, you will have no trouble with this book, and you'll do quite well in the obligatory Mathematical Economics course they have in most Ph.D. programs.

You do not need to study up on more esoteric topics such as Game Theory or International Trade before you enter a Ph.D. program, although it never hurts to do so. You are not usually required to have a background in those subject areas when you take a Ph.D. course in them. I will recommend a couple of books I greatly enjoy, as they may convince you to study these subjects. If you're at all interested in Public Choice Theory or Virginia style Political Economy, first you should read my article " The Logic of Collective Action ". After doing so, you may want to read the book Public Choice II by Dennis C. Mueller. It is very academic in nature, but it is probably the book that has influenced me most as an economist. If the movie A Beautiful Mind didn't make you frightened of the work of John Nash you may be interested in A Course in Game Theory by Martin Osborne and Ariel Rubinstein. It is an absolutely fabulous resource and, unlike most books in economics, it's well written.

If I haven't scared you off completely from studying economics , there's one last thing you'll want to look into. Most schools require you to take one or two tests as part of your application requirements. Here's a few resources on those tests:

Get familiar with the GRE General and GRE Economics Tests

The Graduate Record Examination or GRE General test is one of the application requirements at most North American schools. The GRE General test covers three areas: Verbal, Analytical, and Math. I've created a page called "Test aids for the GRE and GRE Economics" that has quite a few useful links on the GRE General Test. The Graduate School Guide also has some useful links on the GRE. I would suggest buying one of the books on taking the GRE. I can't really recommend any one of them as they all seem equally good.

It is absolutely vital that you score at least 750 (out of 800) on the math section of the GRE in order to get into a quality Ph.D. program. The analytical section is important as well, but the verbal not as much. A great GRE score will also help you get into schools if you have only a modest academic record.

There are a lot fewer online resources for the GRE Economics test. There are a couple of books that have practice questions that you may want to look at. I thought the book The Best Test Preparation for the GRE Economics was quite useful, but it's gotten absolutely horrid reviews. You may want to see if you can borrow it before committing to buying it. There is also a book called Practicing to Take the GRE Economics Test but I've never used it so I'm not sure how good it is. It is important to study for the test, as it may cover some material that you did not study as an undergraduate. The test is very heavily Keynesian, so if you did your undergraduate work at a school heavily influenced by the University of Chicago such as the University of Western Ontario, there will be quite a bit of "new" macroeconomics you'll need to learn.

Economics can be a great field in which to do your Ph.D., but you need to be properly prepared before you enter into a graduate program. I haven't even discussed all the great books available in subjects such as Public Finance and Industrial Organization.

• Choosing the Best Economics Graduate Program
• What Is Mathematical Economics?
• Why Get an Economics Ph.D?
• Real Analysis
• What You Should Know Before Applying to an Economics PhD Program
• Positive Versus Normative Analysis in Economics
• What Is International Economics?
• Ace Your Econometrics Test
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Next Gen PhD: A Guide to Career Paths in Science 1st Edition

For decades, top scientists in colleges and universities pursued a clear path to success: enroll in a prestigious graduate program, conduct research, publish papers, complete the PhD, pursue postdoctoral work. With perseverance and a bit of luck, a tenure-track professorship awaited at the end. In today’s academic job market, this scenario represents the exception. As the number of newly conferred science PhDs keeps rising, the number of tenured professorships remains stubbornly stagnant. Only 14 percent of those with PhDs in science occupy tenure-track positions five years after completing their degree.

Next Gen PhD provides a frank and up-to-date assessment of the current career landscape facing science PhDs. Nonfaculty careers once considered Plan B are now preferred by the majority of degree holders, says Melanie Sinche. An upper-level science degree is a prized asset in the eyes of many employers, and a majority of science PhDs build rewarding careers both inside and outside the university. A certified career counselor with extensive experience working with graduate students and postdocs, Sinche offers step-by-step guidance through the career development process: identifying personal strengths and interests, building work experience and effective networks, assembling job applications, and learning tactics for interviewing and negotiating―all the essentials for making a successful career transition.

Sinche profiles science PhDs across a wide range of disciplines who share proven strategies for landing the right occupation. Current graduate students, postdoctoral scholars, mentors, and students considering doctoral and postdoctoral training in the sciences will find Next Gen PhD an empowering resource.

• ISBN-10 0674504658
• ISBN-13 978-0674504653
• Edition 1st
• Publisher Harvard University Press
• Publication date August 22, 2016
• Language English
• Dimensions 5.75 x 0.5 x 8.5 inches
• Print length 272 pages
• See all details

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• Publisher ‏ : ‎ Harvard University Press; 1st edition (August 22, 2016)
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MACROECONOMICS

A comprehensive textbook for first-year Ph.D. courses in macroeconomics.

M. Azzimonti, P. Krusell, A. McKay, and T. Mukoyama

with T. Boppart, G. Corsetti, L. Dedola, J. Hassler, J. C. Hatchondo, J. Heathcote, A. Hornstein, P. Klenow, S. Lloyd, L. Martinez, K. Mitman, C. Olovsson, M. Piazzesi, V. Quadrini, M. Ravn, R. Rogerson, V. Rios-Rull, A. Sahin, M. Schneider, K. Storesletten, and G. Violante.

Our Approach

The book presents modeling and analytical tools of macroeconomic analysis and then applies them to the main topics within the field of macroeconomics.

Data Driven – A key feature is that we make explicit connection with data throughout the book. Macro theory is used to interpret the data.

Research Orientated – The book has the rigor of a Ph.D. course, but the material is presented in an accessible way, making it suitable to a wide range of students, including those in MA and MS courses.

Expert Led – The material in the book represents the state of the art knowledge of the field. Chapters 1-9 are written by the core authors. Chapters 10-24 are written by or co-authored with leading experts on particular topics.

The first 9 chapters discuss core topics typically covered in a first-semester graduate macroeconomics sequence. A subset of the following chapters are usually covered in the second semester. They are self-contained, providing versatility to instructors to choose which material to focus one, while at the same time connecting concepts to the first section of the book.

READ CHAPTER SUMMARY

Appendices and Other Material

Proofs and additional material is placed in chapter appendices. These are intended to expand on the topic for students and instructors who are interested in going in depth.

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Latest News

We’ve added a Tools chapter with Continuous Time and Computational Methods

“The consequences for human welfare involved in questions like these are simply staggering: once one starts to think about them, it is hard to think about anything else.”

The Authors

This book brings together a set of leading experts on a variety of topics which are relevant to modern economists’ core knowledge.

Toshihiko Mukoyama

Alisdair McKay

Per Krusell

Marina Azzimonti

Timo Boppart

Giancarlo-Corsetti

Gianluca Violante

Kjetil Storesletten

Luca Dedola

Vincenzo Quadrini

John Hassler

Simon lloyd.

Kurt Mitman

Martin Schneider

Leo Martinez

Monika Piazzesi

Pete Klenow

Andreas Hornstein

Jonathan Heathcote

J. c. hatchondo.

Victor Rios-Rull

Conny olovsson.

Richard Rogerson

Aysegul Sahin

Morten Ravn

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20 Best Research Methodology Books for Ph.D. Students

As a Ph.D. candidate, research methodology is of the utmost importance for the completion of your degree. Books on research can be an invaluable resource to Ph.D. students. These will help you with researching books, improving your planning, and help you to identify the most professional dissertation writers. If you would like to learn more about the best research books for Ph.D. students, then the following article will be your guide. 

1. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4 th Edition

The hallmark of this textbook is that it describes and compares the three main types of research methodology as well as the writing involved. This makes it quite different to many other books and services targeting Ph.D. students. The world’s changing and most dissertation writing from Ph.D. writers from EDUbirdie are not focused on singular methods anymore. And that is what you will find in this book – insights, and support for any method that you are pursuing.

 This makes it far easier to understand and select the concept that fits your study best. The textbook goes one step further by also having a philosophical conversation about research methodology. As such, it explores ethical and moral concerns, in addition, to logistical ones. This makes the book a great deal more well-rounded than its literary counterparts. 

2. The Craft of Research, 3 rd Edition

Even works produced by top Ph.D. writing services can be difficult to understand, particularly for layman readers. This is because the thesis and resulting work haven’t been properly explained. This textbook helps to correct this by showing you how to properly outline your argument and the supporting evidence. In doing so, you will find that you are better equipped to write a more compelling paper. 

3. Qualitative Inquiry and Research Design: Choosing Among Five Approaches, 3 rd Edition

If you are focused on qualitative research methodology, then this textbook should be at the very top of your list. It breaks down the main five approaches to a qualitative inquiry by looking at the fundamental elements of each one of them. The author offers even more support by giving you guidelines on constructing your ideas as well as improving the standard of your work. 

4. Doing Your Research Project (Open Up Study Skills), 5 th Edition

This is the textbook that you should be reaching for if you want to get on the same level as good Ph.D. writing services. It is especially useful for those who have only just begun their Ph.D. journey. This textbook contains crucial information on the most basic of skills . This includes preparing for your research, drafting your paper, and putting the finishing touches on it. 

6. The Essential Guide to Doing Your Research Project, 2 nd Edition

If you are feeling rusty regarding any of your research methodology, then this textbook can help you out. This is undoubtedly one of the more comprehensive books on research. All the stages of the research process are broken down and the text even includes summaries, glossaries, and much more. 

7. Naturalistic Inquiry, 1 st Edition

If your research topic is based on the field of social science, then this is a top book for Ph.D. students. For one thing, it challenges traditional approaches and proposes more progressive and accurate forms of study. Following the concepts and advice of this book could lead to more accurate results. 

8. Qualitative Research: A Guide to Design and Implementation, 4 th Edition

This book offers the latest insight into qualitative research. As such, you will be able to move your study and thesis into a new era. The text should also give you better insight into researching books for your thesis, creating a modern approach to your work.

9. The Research Methods Knowledge Base, 3 rd Edition

This is a great textbook, regardless of the field that you are in. It offers up comprehensive coverage of both qualitative vs quantitative research methods. The language in the book is equally accessible to both novices as well as professional dissertation writers. This book will help to clear up any questions or confusion you may have. 

10. Introducing Research Methodology: A Beginner’s Guide to Doing a Research Project 2 nd Edition

As the name suggests, this is an excellent guide to those who are just starting out with their research project. Whether you need to brush up on the subject matter, improve your overall approach, or would like to create a more structured concept, this book will help you in all these areas. It will be like hiring your own dissertation writing services. 

11. The SAGE Handbook of Qualitative Research, 4 th Edition

There is no denying that research needs to be more diverse than ever before. If this is a concept that you would like to include in your work, this textbook can help you. Here, qualitative research is given a social spin and is applied to more real-world terms. As such, it can improve the quality and accuracy of your current and future work. 

12. The Foundations of Social Research: Meaning and Perspective in the Research Process

There is quite a bit of variation in schools of thought, terminology, and more when it comes to social research. This textbook takes the trouble to break all these down and discuss the discrepancy. In turn, this makes it far easier for you to get a more comprehensive understanding of your next step in researching books. 

13. Essentials of Research Design and Methodology

If you want fuss-free assistance on selecting research and creating an efficient research plan, this textbook will help you out. There is a lot of information available in data collection, assessment strategies, interpretation methods, and more. 

14. Introduction to Quantitative Research Methods: An Investigative Approach

In case you are having trouble grasping various concepts of quantitative research methods, you will find this book rather useful. This is because the authors take a different approach to handling these topics. They tackle each concept like detectives and use real-world problem-solving schematics. Thus, it functions as an excellent Ph.D. writing service. 

15. Research Justice 

For research to be applicable to a real-world scenario, it must appeal to all demographics. This book shows you how to create a thesis and carry out research so that you are creating a more diverse group of participants. In doing so, you make your research far more relevant by modern standards. 

16. Single Case Research Methodology, 3 rd Edition

It doesn’t matter if you are a Ph.D. student, researcher, or even a professional practitioner. This book will guide you through all aspects of single case research methodology. With the help of this text, you can conduct single-case design studies, interpret findings, write proposals, and a whole lot more. 

17. Qualitative Dissertation Methodology: A Guide for Research Design and Methods, 1 st Edition 

One of the more useful aspects of this book is that it is based on actual students’ experiences. Thus, it adequately tackles all the obstacles that you may come across when researching books, writing proposals, or doing actual research. The book breaks down all elements of qualitative research into smaller parts, making it more manageable for students. 

18. Research and Publications Planner: The Graduate Student’s Guide to Publishing Academic Research

This book is written by a graduate student. Thus, it appreciates the real-world struggles of coming up with research ideas and then executing your vision. The book guides you through every step of the way, making it easy for you to structure and organize your work so that you are creating a more cohesive document. 

19. Doing Academic Research: A Practical Guide to Research Methods and Analysis

This book is suitable for students that are looking for books on research in any field. It doesn’t matter if you are humanity, business, or social science – this book will appeal to you. As the title suggests, this is a practical guide. Therefore, it will provide you with relevant information and assistance every step of the way. 

20. Case Study Research and Applications: Design and Methods, 6 th Edition

If you are engaged in case study research, then you should check out this book. This is because it uses numerous real-world case studies to give you a clearer idea of how to write, analyze , and come to your own conclusions with your current work. The writer also offers up suggestions for improvements as well as how to improve the accuracy of your research. 

21. Research Methods: A Practical Guide for Students and Researchers 

This book allows you to do research in an organized and concise manner. It starts from the very beginning of your research process and gives you tips and suggestions that are useful at every stage. Furthermore, it gives you real-world examples to describe what is being explained in the book. This is a suitable option for students across all disciplines. 

These are the top research methodology books for Ph.D. students to invest in. It doesn’t matter what discipline you are in or what kind of research you are doing. You can guarantee that at least one of these books will give you the guidance and answers that you are looking for. 

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Best Books for PhD Students: Top 10 Must-Reads to Succeed

Find 11 AI tools to help students learn better, work faster, and study smarter. Use AI to do well in school and prepare for your future success.

Derek Pankaew

May 21, 2024

The best books for PhD students can be invaluable companions, navigating the journey is no easy feat, which is why having the right resources can be a game-changer.

These books, along with journal articles, are invaluable across different stages of graduate school .

They address mental health challenges and offer practical advice for both academic and career success.

To support this ambitious academic pursuit, we’ve curated a list of ten essential reads that promise to enlighten, guide, and inspire PhD students. These selections stand out for their timeless relevance and comprehensive insights into the rigors of higher education.

Top 10 Recommended Books for PhD Students

1. how to write a lot: a practical guide to productive academic writing by paul j. silvia.

Photo by GoodReads

Book Description:

Silvia dismantles the myths surrounding academic writing and provides practical advice on how to write more and better. A must-read for scholars in any discipline, this book offers concrete tips on finding time to write, setting achievable goals, and productive academic writing .

Why We Recommend this Book:

Silvia’s approachable and actionable advice makes this book an invaluable resource for PhD students looking to refine their academic writing habits and output.

Firstly, Silvia's book is practical advice for graduate students because it addresses the psychological barriers that prevent many from starting their writing projects. Recognizing and overcoming these hurdles is key to consistent productivity.

Secondly, the book's focus on developing a sustainable writing routine is invaluable.

By demystifying the writing process, Silvia aids students in integrating writing into their daily lives, ensuring steady progress toward their academic goals.

2. How to Get a PhD: A Handbook for Students and Their Supervisors by Estelle M. Phillips and Derek S. Pugh

Photo by Google Books

Book Overview:

This comprehensive guide outlines the PhD process from start to finish. From selecting a topic and supervisor to conducting research and writing your thesis, Phillips and Pugh give good advice and provide an indispensable roadmap for aspiring PhD candidates.

With its thorough exploration of the PhD lifecycle, this book is an essential companion for any graduate student seeking clarity and direction on their academic journey.

It specifically addresses the unique challenges and opportunities presented during graduate school, making it a vital resource for navigating the complexities of graduate studies.

Notably, Phillips and Pugh’s handbook stands out as a must-read for PhD students due to its emphasis on the interpersonal aspects of the PhD journey, such as navigating relationships with supervisors and peers.

This focus is crucial because successful collaboration and networking are pivotal for academic and professional growth.

Additionally, the book’s pragmatic advice on handling the emotional challenges of academia equips students with strategies for resilience, making it an invaluable tool for maintaining well-being throughout the demanding process of earning a PhD.

3. The Craft of Research by Wayne C. Booth, Gregory G. Colomb, and Joseph M. Williams

Photo by The University of Chicago Press

Aimed at researchers at all levels in their research career, this book provides a step-by-step guide to designing and executing a research project and academic writing.

It covers everything from forming a research question to gathering evidence and making a persuasive argument.

The Craft of Research is a fundamental tool for PhD students that offers deep insights into the intricacies of scholarly investigation and narrative crafting.

Ethical research practices are particularly important in fields like computer science, where the latest cutting-edge research prepares students and seasoned researchers for impactful careers.

The Craft of Research is particularly invaluable for PhD students for several reasons. Firstly, it demystifies the research process, breaking down complex concepts into digestible, actionable steps that foster confidence and efficiency in academic inquiry.

This aspect is crucial for students who may be overwhelmed by the scope of their research projects.

Secondly, the emphasis on ethical research practices within the book instills a strong foundation of integrity in students, ensuring that their work not only contributes to their field but also adheres to the highest standards of academic conduct.

These elements make The Craft of Research an indispensable guide for those navigating the challenging waters of doctoral research.

Additionally, the book’s guidance on designing and executing research projects can inspire PhD students to pursue their curiosity-driven scientific research, enhancing their productivity and personal development in their area of expertise.

4. Writing Your Dissertation in Fifteen Minutes a Day by Joan Bolker

Photo by Macmillan Publishers

Bolker offers an encouraging guide for making consistent progress on your dissertation .

By breaking down the writing process into manageable chunks, this book promotes a daily habit that can help you overcome procrastination and anxiety and improve your well-being as a grad student.

Bolker's empathetic and realistic advice is particularly beneficial for PhD students struggling with writer’s block, staying organized or time management issues.

Joan Bolker's method not only provides a structured framework to approach the monumental task of dissertation writing but also instills a sense of accountability and routine in PhD students.

This approach is pivotal in transforming what can often seem like an insurmountable project into a series of achievable goals, thereby demystifying the process of completing your thesis.

Another critical advantage of this guide is its focus on the psychological aspects of writing and research.

Recognizing and addressing the common emotional hurdles that PhD students face—such as imposter syndrome, isolation, and burnout—Bolker offers strategies to maintain motivation and mental health throughout the demanding course of doctoral studies.

These reasons underscore why "Writing Your Dissertation in Fifteen Minutes a Day" is an essential read for anyone at the beginning or in the throes of their doctoral research journey.

5. The Professor Is In: The Essential Guide to Turning Your Ph.D. Into a Job by Karen Kelsky

Photo by The Professor Is In

Kelsky provides a frank and detailed guide to navigating the academic tenure track position.

Covering topics like crafting your CV, acing interviews , and understanding the intricacies of academic employment, this book is crucial for anyone looking to transition from PhD student to employed scholar.

Kelsky’s insights stem from years of experience within academia, allowing her to offer practical advice that is often not covered in traditional academic settings.

She breaks down the complex job market, providing PhD students with actionable steps to stand out in a competitive environment.

Additionally, the book’s emphasis on adapting academic skills for a variety of career paths makes it indispensable for students who are considering both academic and non-academic careers .

Her direct approach encourages students to proactively shape their career trajectory well before graduation, thereby enhancing their employability and ensuring a smoother transition into post-PhD life.

For those aspiring to secure tenure track positions, Kelsky offers guidance that navigates the nuanced path towards achieving such esteemed roles.

Furthermore, Kelsky shares valuable lessons from her experience, equipping PhD students with the knowledge to navigate the academic job market successfully.

Easily pronounces technical words in any field

6. PhD: An Uncommon Guide to Research, Writing & PhD Life by James Hayton

James Hayton demystifies the complexities of the PhD process, offering straightforward advice on getting through the challenges of research and writing.

The book also touches on the less-discussed aspects of PhD life, such as coping with stress and uncertainty.

Hayton's personal anecdotes and pragmatic advice make this guide a comforting companion during the roller-coaster ride of a PhD.

This book is particularly beneficial for PhD students because it goes beyond the academic and technical aspects of the PhD process, addressing the emotional and psychological challenges that are often overlooked.

Hayton's openness about his own struggles and successes provides readers with a sense of camaraderie, making the PhD journey feel less isolating.

Furthermore, his practical strategies for dealing with common issues like procrastination, writer's block, and imposter syndrome are invaluable resources that can help students maintain momentum and confidence throughout their studies.

7. A Manual for Writers of Research Papers, Theses, and Dissertations by Kate L. Turabian

Description:

Turabian’s manual has been the go-to resource for generations of scholars. This comprehensive guide covers all aspects of the research process, including citation practices, grammar, and style.

A timeless reference that every PhD student should have at their fingertips, especially during the thesis and dissertation phase.

First, Turabian's manual serves as an essential roadmap for navigating the complexities of research writing, ensuring that students adhere to the highest standards of scholarship.

Its detailed guidelines on citation practices help students avoid the pitfalls of plagiarism, fostering academic integrity.

Second, the manual's emphasis on clear, concise, and coherent writing is invaluable for scholars aiming to communicate their research findings effectively.

By following Turabian's principles, students can enhance the clarity and impact of their work, making it more accessible to fellow academics and the broader public.

8. The Elements of Academic Style: Writing for the Humanities by Eric Hayot

Photo by Columbia University Press

Hayot offers an insightful guide to writing effectively in the humanities, focusing on the audience, the rhythm of writing, and argumentation.

This book encourages scholars to think about the purpose and impact of their academic writing.

Essential reading for PhD students in the humanities, providing deep reflections on the craft of writing and scholarly communication.

Hayot's work is indispensable for PhD students for multiple reasons. Firstly, it demystifies the process of crafting a compelling academic argument, which is central to producing impactful research.

Hayot’s emphasis on understanding the audience's expectations and the rhythm of one's writing fosters a nuanced approach to scholarship that resonates well beyond the academic sphere.

Secondly, the book encourages scholars to elevate their writing from merely informative to truly engaging, encouraging them to weave complexity and sophistication into their work.

This approach not only bolsters the students' ability to articulate their ideas but also significantly enhances their contribution to academic discourse.

9. Academic Writing for Graduate Students: Essential Tasks and Skills by John M. Swales and Christine B. Feak

Photo by University of Michigan Press

This book is a comprehensive guide to the genre of academic writing. It’s specifically designed for non-native English speakers, offering clear instructions on everything from crafting a research paper to participating in academic discussions.

Swales and Feak have created an invaluable resource that addresses the unique challenges faced by international PhD students.

The book's approach to breaking down the academic writing process into manageable tasks is particularly beneficial for PhD students who may feel overwhelmed by the demands of their research projects.

By providing clear, step-by-step guidance, Swales and Feak empower students to improve their writing skills systematically, enhancing their ability to communicate complex ideas effectively.

Additionally, the focus on the needs of non-native English speakers fills a critical gap in academic writing resources, making it a must-read for international students who are striving to meet and exceed the rigorous standards of academic discourse in English-speaking environments.

10. Publish or Perish: Perceived Benefits versus Unintended Consequences by Imad A. Moosa

Photo by Amazon

Moosa critically examines the pressure to publish in academia, exploring the impact on academic research's quality and integrity. This thought-provoking book encourages readers to rethink the publish-or-perish culture.

A compelling read for PhD students and seasoned academics alike, prompting important reflections on the values and practices of modern academic life.

Moosa's insightful analysis serves as a crucial wake-up call for PhD students, highlighting the potential pitfalls of succumbing to the publish-or-perish mentality that pervades many academic institutions .

This book is particularly valuable for doctoral candidates as it not only illuminates the ways in which this pressure can compromise the quality and integrity of one's research but also offers practical strategies for navigating academic publishing more ethically and effectively.

By fostering a deeper understanding of the implications of current publishing demands, it equips students with the knowledge to prioritize meaningful research contributions over mere quantity, promoting a healthier, more sustainable approach to academic success.

Expand Your PhD Reading List

Expanding your reading list to include the latest research and developments in your field can be invaluable.

While navigating the vast academic literature can be daunting, the works mentioned above serve as a lighthouse, guiding scholars through the often turbulent waters of doctoral studies.

Beyond these books, it is crucial for students to engage with their research communities, seek mentorship, and participate in academic conferences. These activities not only enrich one’s understanding of their field but also foster invaluable networks that can support career development long after graduation.

Additionally, developing skills in science writing and scientific writing is essential for opening up career opportunities both within and outside academia.

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Recommendations for statistical mechanics book

I learned thermodynamics and the basics of statistical mechanics but I'd like to sit through a good advanced book/books. Mainly I just want it to be thorough and to include all the math. And of course, it's always good to give as much intuition about the material.

Some things I'd be happy if it includes (but again, it mostly just needs to be a clear book even if it doesn't contain these) are:

As much justifications for the postulates if possible, I'm very interested in reading more about how Liouville's theorem connects to the postulates.

Have examples of calculating partition functions, hopefully not just the partition function for the ideal gas.

• thermodynamics
• statistical-mechanics
• mathematical-physics
• resource-recommendations
• 2 $\begingroup$ A good advanced book that covers in details and with mathematical rigor what you want and much more is Gallavotti's "Statistical Mechanics - a short treatise", which is not so short actually... You can get it from here . $\endgroup$ –  Yvan Velenik Commented Jun 21, 2012 at 20:07
• 2 $\begingroup$ Another good (but probably too advanced) book is the "old" book by Ruelle, "Statistical Mechanics - Rigorous Results". If you have the level in maths, and are interested in the mathematical theory of phase transitions for lattice systems, the classical reference is Georgii's "Gibbs measures and phase transitions" (although that's more graduate level stuff). $\endgroup$ –  Yvan Velenik Commented Jun 21, 2012 at 20:09
• 1 $\begingroup$ Just in case. Here are the google book pages for the last 2 refs, so that you can have an idea of what is done there and at which level: Ruelle , Georgii . $\endgroup$ –  Yvan Velenik Commented Jun 22, 2012 at 16:15
• 2 $\begingroup$ I just stumbled on this old question. As a complement to the previous comments, you could also look at this answer , in which I list many more mathematically rigorous references. $\endgroup$ –  Yvan Velenik Commented Aug 27, 2022 at 9:33

10 Answers 10

EDIT: My answer assumes that you're looking for a book at the introductory graduate level .

I found Pathria's "Statistical Mechanics" (2nd ed) very helpful during my first-year graduate statistical mechanics course. Pathria's treatment of the subject is mathematically careful and detailed, at least by physics standards; I found his discussion of Liouville's theorem (part 1 of your question) satisfactory. Unfortunately, like many formal treatments, Pathria discusses few interesting applications.

"Statistical Physics of Particles" by Kardar appears to be supplanting Pathria as the favored introductory graduate text; it was used at Boston University and at Caltech during my time there. Kardar is very terse and would probably have to be supplemented by another book, but the problems he offers are interesting (if hard). In fact, about a third of the text consists of detailed solutions to the problems.

I have heard good things about Reichl's book, already mentioned in another answer. I used it briefly as a reference: the coverage of kinetic theory is more complete than in other sources. It is more accessible than Pathria, not to mention Kardar.

I recommend the book A Modern Course in Statistical Physics by Reichl . It starts with phenomenological thermodynamics, covers both equilibrium and nonequilibrium statistical mechanics, and discusses a wide range of applications, not only ideal and real gases. Its level of rigor is that of typical books on theoretical physics.

You may also be interested in my online book https://arxiv.org/abs/0810.1019 the part on statistical mechanics is nearly independent of the remainder.

• $\begingroup$ broken link, please update working link $\endgroup$ –  Saleem Ahmed Commented Oct 4, 2022 at 3:02

As an undergrad, we used "Thermal Physics" by Kittel and Kroemer:

http://www.amazon.com/Thermal-Physics-Edition-Charles-Kittel/dp/0716710889

I recommend books by Kardar "Statistical Physics of Particles" "Statistical Physics of Fields" The mordern approach to this subject is helpful for your future study.

Also there are solutions to all of the problem, which you can find from the internet.

Greiner's Thermodynamics and Statistical Mechanics is pretty good from a few short readings I did. Also, it has better reviews from almost all of the other popular textbooks on the subject in goodreads.com

If anyone is interested in seeing how this is done from a chemist's perspective I can heartily recommend Statistical Mechanics: Theory and Molecular Simulation by Mark Tuckerman. Sadly, it isn't on line but can be ordered from Amazon or the like.

• $\begingroup$ this is an exeptionally good bok if your interested in getting a second look. (At least thats what I am using in it for) $\endgroup$ –  Kuhlambo Commented Feb 2, 2016 at 22:02

It has to be " Statistical Mechanics and Thermodynamics " by Claude Garrod". You can use the text by Macquarie as a supplement. For renormalization group and advanced concepts, use " Statistical Physics of Fields" by Kardar.

It may sound old but 0."An introduction to statistical physics- by A.J. Pointon" is a very handy book to absorb the concept of calculation over phase space from the very beginning. The book is suitable for a one semester course, designed for last year undergraduate and beginning graduate students. The exposition of this book is exceptionally clear. It hardly skips any mathematics under the hood.

For more advanced treatment of the subject there are plenty of other good books. The list is not exhaustive at all..

• Statistical Mechanics, 2nd Edition by Kerson Huang
• A Modern Course in Statistical Physics 4th ( & 2nd) Edition by Linda E. Reichl
• Statistical Physics: Volume 5 3rd Edition by L.D. Landau, E. M. Lifshitz
• Statistical Physics of Particles 1st Edition by Mehran Kardar
• Statistical Mechanics 3rd Edition by R K Pathria, Paul D. Beale

One may also get interest into the book - 6. Introduction to Modern Statistical Mechanics 1st Edition by David Chandler. The approach of this book to the subject is very different than the above mentioned books.

I'll assume that by advanced you mean advanced undergrad, the other answers seem to mix different level books.

I find that most advanced physics (not just stat mech) books range from bad to awful. Sadly, most standard books used on courses fall into this category, i see some of them in the other comments (like Greiner and Pathria).

Also, the two books i consider best are not mentioned in any of the comments.

Theory books

I'll list them from simplest to hardest.

Tong, Statistical physics lectures : Excellent for a first look on stat mech as an advanced undergrad. Free, available in the link. excellent prose. Good conceptual explanations. It covers all the basic results. For more advanced things or in depth, you'll need to consult other sources. Overall the best option.

Reif, Fundamentals of statistical and thermal physics: Not just good explanations, also makes you know which are the important things on each subject. Excellent prose, great conceptual explanations. A bit old. Excellent for most topics. The last part covers advanced kinetic theory, irreversible processes and fluctuations. I'd check this one when Tong's lectures are not enough.

Landau and Lifshitz, Statistical Physics: Volume 5: Just as Reif, it lets you know which things are important, but indirectly. Basically, if it isn't important, it's not in this book. The writing is excellent. Good explanations. Focus on central concepts. A lot of subjects are treated differently to standard books, usually simpler and relying more on physical grounds. Won't lose time over non important things. Almost every subject's simplest solution is found in this book. Too hard for humans. I'd take this one as the third option, unless you are well versed in the subject of interest.

One of your requests is:

For this, i'd recommend a special book only for examples:

Example books

Cini, Fucito and Sbragalia, Solved Problems in Quantum and Statistical Mechanics: lots of examples, it has 200 pages of solved problems in stat mech. Decent explanations, way better than other more known solved problems books, like Kubo or Dalvit.

Another request was:

Mainly I just want it to be thorough and to include all the math.

Greiner books

Greiner, Stöcker, Neise and Rischke, Thermodynamics and statistical mechanics: It is thorough in the math steps and it has a lot of examples for a theory book. Explanations are bad. Writing not good. Physical arguments and concepts nowhere to be found. Will treat the most important results (for example, the canonical/Boltzmann distribution) in the same way as the most superfluous details. Derivation of quantum statistics is specially bad. Usually I don't recommend it at all, but you may find it useful to check specific math steps or examples.

If you know French, the way to go is:

• Eléments de physique statistique by Bernard Diu, Claudine Guthmann and ‎Danielle Lederer.

The first author is the same co-author from the exemplary Cohen-Tannoudji's Quantum Mechanics series. It is very complete and with a lot of complementary chapters. It is sad that there is still no English version.

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5 books to help you with your PhD

There’s so many, many books on the market that claim to help you with your PhD – which ones are worth buying? I have been thinking about it this topic for some time, but it’s still hard to decide. So here’s a provisional top 5, based on books I use again and again in my PhD workshops:

I wish I owned the copyright to this one because I am sure they sell a shed load every year. Although it seems to be written for undergraduates, PhD students like it for its straight forward, unfussy style. Just about every aspect of research is covered: from considering your audience to planning and writing a paper (or thesis). The section on asking research questions is an excellent walk through of epistemology: an area many people find conceptually difficult. I find it speaks to both science and non science people, but, like all books I have encountered in the ‘self help’ PhD genre, The Craft of Research does have a bias towards ‘traditional’ forms of research practice. You creative researcher types might like to buy it anyway, if only to help you know what you are departing from.

2. How to write a better thesis by Paul Gruba and David Evans

This was the first book I ever bought on the subject, which probably accounts for my fondness for it. I have recommended it to countless students over the 6 or so years I have been Thesis Whispering, many of whom write to thank me. The appealing thing about this book is that it doesn’t try to do too much. It sticks to the mechanics of writing a basic introduction> literature review> methods> results> conclusion style thesis, but I used it to write a project based creative research thesis when I did my masters and found the advice was still valid. Oh – and the price point is not bad either. If you can only afford one book on the list I would get this one.

3. Helping Doctoral Students to write by Barbara Kamler and Pat Thomson

I won an award for my thesis and this book is why. In Helping doctoral students to write Kamler and Thomson explain the concept of  ‘scholarly grammar’, providing plenty of before and after examples which even the grammar disabled like myself can understand. I constantly recommend this book to students, but I find that one has to be at a certain stage in the PhD process to really hear what it has to say. I’m not sure why this is, but if you have been getting frustratingly vague feedback from your supervisors – who are unhappy but can’t quite tell you why – you probably need to read this book. It is written for social science students, so scientists might be put off by the style – but please don’t let that stop you from giving it a go. Physicists and engineers have told me they loved the book too. If you want a bit more of the conceptual basis behind the book, read this earlier post on why a thesis is a bit like an avatar.

4. The unwritten rules of PhD research by Marian Petre and Gordon Rugg

I love this book because it recognises the social complexities of doing a PhD, without ever becoming maudlin. Indeed it’s genuinely funny in parts, which makes it a pleasure to read. The authors are at their best when explaining how academia works, such as the concept of ‘sharks in the water’ (the feeding frenzy sometimes witnessed in presentations when students make a mistake and are jumped on by senior academics) and the typology of supervisors. It’s also one of the better references I have found on writing conference papers.

5. 265 trouble shooting strategies for writing non fiction Barbara Fine Clouse

This book is great because it doesn’t try to teach you how to write – you already know how to do that. What you need more is something to help you tweak your writing and improve it. This book is basically a big list of strategies you might like to try when you are stuck, or bored with the way you are writing. This book is so useful I have literally loved it to death – the spine is hopelessly broken and pages are held in by sticky tape. There are many wonderful tips in here from ‘free writing’ and ‘write it backwards’ ideas, to diagramming methods and analytical tools. Opening it at almost any page will give you an idea of something new to try.

What books would be on your top 5 list and why?

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The Thesis Whisperer is written by Professor Inger Mewburn, director of researcher development at The Australian National University . New posts on the first Wednesday of the month. Subscribe by email below. Visit the About page to find out more about me, my podcasts and books. I'm on most social media platforms as @thesiswhisperer. The best places to talk to me are LinkedIn , Mastodon and Threads.

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A comprehensive book on graduate real analysis

I'm looking for a comprehensive book/a comprehensive list of books on graduate analysis that covers/cover these topics: Lebesgue measure and integration on $\mathbb{R}^d$ , the relationships between integrability and differentiability (it must also cover the theory of functions of bounded variation), complex analysis and fourier analysis.

• real-analysis
• integration
• complex-analysis
• fourier-analysis
• real-numbers

• 5 $\begingroup$ Stein and Shakarchi's princeton lectures in analysis is excellent $\endgroup$ –  fwd Commented Sep 28, 2021 at 16:54
• 2 $\begingroup$ Integration and Modern Analysis by Benedetto/Czaja (2009; 575 pages) is pretty comprehensive (525 references, 14 page subject index, 8 page index of names) and includes a 42 page appendix on Fourier analysis. For complex analysis I strongly recommend getting a separate text, since the subject matter is sufficiently different that you're not going to find a comprehensive book for both (although Rudin is worth looking at). $\endgroup$ –  Dave L. Renfro Commented Sep 28, 2021 at 18:27

5 Answers 5

The following books might be interesting in your case too:

• Mathematical Analysis (Apostol), since it covers Lebesgue measure and similar topics
• Real Analysis: A Comprehensive Course in Analysis, Part 1 (Berry Simon) is a very good reference too (I really like AMS books)
• A Passage to Modern Analysis (W. J. Terrell) is from AMS too and introduces things more gently (including Lebesgue measures) since it is from the series "Pure and Applied Undergraduate Texts"
• Manifolds and Differential Geometry (J. M. Lee) covers additionally smooth categories

The following books are available as PDF:

• Real Analysis (4th Ed.) by Royden
• Measure, Integration & Real Analysis (Open Access Book) by Sheldon Axler as already mentioned by Axion004
• Basic Analysis: Introduction to Real Analysis (free online textbook) by Jiří Lebl
• Basic Real Analysis (Stony Brook Mathematics)

Here is a chapter on Lebesgue Integral (the whole book Lectures on Real Analysis might be interesting, since one can see at least the chapter titles including descriptions of its content):

• The Lebesgue Integral

Real Analysis: Modern Techniques and Their Applications by Gerald B. Folland. It presupposes and sparingly applies some complex analysis (as opposed to discussing the fundamentals), but the other mentioned topics are covered extensively from scratch.

• $\begingroup$ My take - It may have been that this was my first graduate-level textbook I had ever read (as a third-year undergrad), but I cannot recommend this book for somebody who is new to measure theory, Lebesgue integration, etc. Far too concise to truly develop intuition. It is a book for somebody looking run through a quick proof and review of familiar material (terse is a good term for this book), but not for a new learner. My personal recommendation would be Rudin's "Real and Complex Analysis", in which a lot of the material in presented in a far more coherent and motivated manner. $\endgroup$ –  JAG131 Commented May 12 at 17:07

You may have used "Baby Rudin" during your undergraduate (aka Principles of Mathematical Analysis by Rudin). He has written a graduate level text, dubbed "Big Rudin", or Real and Complex Analysis. You can find it's official webpage here .

Measure, Integration & Real Analysis by Sheldon Axler. The electronic version is freely available online and the exposition is done in a format similar to Linear Algebra Done Right. The proofs are extremely readable and it has a separate chapter devoted to Fourier Analysis.

I am doing an independent study in measure theory and integration, and I have found Measure and Integral(3rd edition) by Richard L. Wheeden and Antoni Zygmund to be immensely useful. It covers all topics you mention except for a comprehensive treatment of complex analysis. You can find it here: https://www.amazon.com/Measure-Integral-Introduction-Analysis-Mathematics/dp/1498702899/ref=sr_1_2?keywords=measure+and+integral+an+introduction+to+real+analysis&qid=1642651532&sprefix=measure+and+integral+%2Caps%2C125&sr=8-2

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5 must-reads for doctoral students

January 11, 2016

The decision to pursue a doctoral degree can be exciting and scary at the same time.

Good preparation will ease the path to writing a great dissertation. Reading some expert guide books will expand your knowledge and pave the way for the rigorous work ahead.

Capella University faculty, doctoral students, and alumni recommend these five books for doctoral students in any discipline.

1. How to Read a Book: The Classic Guide to Intelligent Reading  by Mortimer J. Adler

“One book fundamental to my doctoral education that my mentor had my entire cohort read, and which I still recommend to this day, is  How To Read a Book , which discusses different reading practices and different strategies for processing and retaining information from a variety of texts.” – Michael Franklin, PhD, Senior Dissertation Advisor, Capella School of Public Service and Education.

Originally published in 1940, and with half a million copies in print,  How to Read a Book  is the most successful guide to reading comprehension and a Capella favorite. The book introduces the various levels of reading and how to achieve them—including elementary reading, systematic skimming, inspectional reading, and speed-reading.

Adler also includes instructions on different techniques that work best for reading particular genres, such as practical books, imaginative literature, plays, poetry, history, science and mathematics, philosophy, and social science works.

2. Dissertations and Theses from Start to Finish  by John D. Cone, PhD and Sharon L. Foster, PhD

This book discusses the practical, logistical, and emotional stages of research and writing. The authors encourage students to dive deeper into defining topics, selecting faculty advisers, scheduling time to accommodate the project, and conducting research.

In clear language, the authors offer their advice, answer questions, and break down the overwhelming task of long-form writing into a series of steps.

3. Writing Your Dissertation in 15 Minutes a Day  by Joan Balker

This book is recommended for its tips on compartmentalizing a large project into actionable items, which can be helpful when working on a project as mammoth as a dissertation. Balker connects with the failure and frustration of writing (as she failed her first attempt at her doctorate), and gives encouragement to students who encounter the fear of a blank page.

She reminds dissertation writers that there are many people who face the same writing struggles and offers strong, practical advice to every graduate student.  Writing Your Dissertation in 15 Minutes a Day  can be applied to any stage of the writing process.

4. From Topic to Defense: Writing a Quality Social Science Dissertation in 18 Months or Less  by Ayn Embar-Seddon O’Reilly, Michael K Golebiewski, and Ellen Peterson Mink

As the authors of this book state, “Earning a doctorate degree requires commitment, perseverance, and personal sacrifice—placing some things in our lives on hold. It is, by no means, easy—and there really is nothing that can make it ‘easy.’”

This book provides support for the most common stumbling blocks students encounter on their road to finishing a dissertation. With a focus on a quick turnaround time for dissertations, this book also outlines the importance of preparation and is a good fit for any graduate student looking for support and guidance during his or her dissertation process.

From Topic to Defense  can be used to prepare for the challenges of starting a doctoral program with helpful tools for time management, structure, and diagnostics.

5. What the Most Successful People Do Before Breakfast: A Short Guide to Making Over Your Mornings—and Life  by Laura Vanderkam

According to author and time management expert Laura Vanderkam, mornings are key to taking control of schedules, and if used wisely, can be the foundation for habits that allow for happier, more productive lives.

This practical guide will inspire doctoral students to rethink morning routines and jump-start the day before it’s even begun. Vanderkam draws on real-life anecdotes and research to show how the early hours of the day are so important.

Pursuing a doctoral degree is a big decision and long journey, but it also can be an exciting and positive experience. Learn more about Capella’s  online doctoral programs .

What's it like to be a doctoral student?

Learn more about the experience, explore each step of the journey, and read stories from students who have successfully earned their doctorate. 

Explore The Doctoral Journey >>

You may also like

Can I transfer credits into a doctoral program?

January 8, 2020

What are the steps in writing a dissertation?

December 11, 2019

The difference between a dissertation and doctoral capstone

November 25, 2019

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Recommended Reading

Note: As an Amazon Associate, I earn from qualifying purchases. In other words, I may earn a small commission if you click on a link on this page to purchase a book from Amazon.

This list is aimed at graduate students, postdocs, and other PhDs who are actively looking for paid employment or exploring career options. It includes both practical resources, books that combine advice with inspiration, ones that hope to advocate for better systems while also breaking things down for job seekers, as well as memoirs and novels. The focus here is on books written for graduate students and PhDs, but I’ve also included what I think as key or otherwise useful texts with a much broader intended audience.

What’s not on this list? Books that focus almost exclusively on graduate school itself are generally omitted (exception: Berdahl and Malloy, for its framing of the whole thing as part of your career). There are great ones in this category, including Jessica McCrory Calarco’s A Field Guide to Grad School , Malika Grayson’s Hooded: A Black Girl’s Guide to the Ph.D. , Robert L. Peters’s Getting What You Came For and Adam Ruben’s Surviving Your Stupid, Stupid Decision to go to Grad School . See also Gavin Brown’s How to Get Your PhD: A Handbook for the Journey , which features an essay by me! Similarly, books that focus on academic careers (once you’ve got one) aren’t included (example: Timothy M. Sibbald and Victoria Handford, eds., The Academic Gateway ), nor are books that focus on navigating a career beyond the ivory tower. There are lots of books about academic writing and publishing, conducting and producing research, doing a dissertation, and related stuff. These aren’t included either.

Something missing? I occasionally update this list, so let me know what you think I should add or change.

Should you be reading for pleasure in graduate school?

Think you only have time to read text books in grad school? That’s what I thought too. You have more time than you think. Your future self will tell you so (trust me). The 5-15 hours and $8-$35 it will take you to read any of these books will pay itself back in time and earnings many-fold throughout your student life and in your first job offer after graduation. Invest in yourself and reap the benefits later.

Don’t set out to read all these books at once. Order 2-3 to start and read them in small doses. Take the day to think about the pages you just read and how they can apply to your life. After you see the changes manifest, come back and find a few other books to continue your journey to becoming your best self.

Follow the links below to have these books in your hands in a few days with Amazon. These are referral links, which means that purchasing these items through these links results in a small percentage of the sale helping to support this blog at no cost to you. We appreciate your support so we can continue putting out helpful content and reviews to help you find the best tools for your research!

* Reminder: Prices on Amazon fluctuate and there are new, used and eBook versions. Follow the links to check the most current prices.

Books to improve your academic writing skills and research output:

How to write a lot: a practical guide to productive academic writing by paul silvia.

This book won’t make you a better writer. It’ll make you a more prolific one. By focusing on good writing habits and drawing clear boundaries between writing time and personal time, you’ll start to turn the excruciating blank page process into a series of small measured successes.

The 2 nd edition includes new sections for advice on grant writing and fellowship proposals, making it a favorite book of many post-docs and new faculty. He also deconstructs every excuse you could ever make for not writing, relying on binge-writing and otherwise procrastinating.

If you follow the advice in this book you should expect benefits to your mental health and work-life balance because you won’t always “feel like you should be writing.” Try it!

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Bird by Bird by Anne Lamott

A bestselling classic about the writing process, writer’s block and the internal obstacles in the writer’s mind. Not specifically about graduate school or academia but is included in this list because it is so highly recommended in the writing community.

The title refers to a short story from her childhood about writing a paper about birds. Like the “How to write a lot” book above, this one encourages a steady and consistent process taking small tasks one at a time. You won’t find a lot of advice about how to write well in this book.

This is written for anyone who struggles with anxiety, perfectionism and paralysis when staring at the blank page and blinking cursor. It’s more of an introspection to ease your nerves with a few exercises to help you get started.

Several Short Sentences About Writing by Verlyn Klinkenborg

A unique book that can help snap you out of typical academic writing mode “…thus the present findings elucidate a novel method for exploring the behavior and interactions of…”

Almost poetic. Almost rhythmic. Straight to the point. The author explains in free form the fallacies and illusions of forming sentences and getting them onto the page. This will force you to re-think your mental process resulting in better sentences and better papers.

The end of the book covers examples of common sentences and calls out the superfluous wording, re-writing it with only the essentials.

Ever had trouble fitting a personal statement into two pages or a proposal into six pages? This is the book for concise and punchy writing. When you can convey more information than your competition, you gain the edge.

Writing Your Dissertation in Fifteen Minutes a Day: A Guide to Starting, Revising, and Finishing Your Doctoral Thesis by Joan Bolker

If you’re lacking motivation, struggling to get started every day or are completely overwhelmed by the massive task at hand, give this book a look. It doesn’t offer any real advice on the details of a dissertation but instead aims to instill confidence in the reader. The author guides you through setting daily page goals, storing ideas and getting something…anything down on the page each day. Essentially a personal confidence coach for writing, applicable to more than just a dissertation.

The Scientist's Guide to Writing: How to Write More Easily and Effectively throughout Your Scientific Career by Stephen B. Heard

A little-known but well-reviewed book on how to improve your science writing. This one also discusses the writing process but with a focus on structuring the story of your paper to clearly convey your experiments, results and conclusions. He often takes a whimsical tone that makes it a fun read. The author breaks down the structure of a scientific paper and the functions of each part. He also dives into the details on submitting, revising and coauthoring scientific papers. This is perhaps the most detailed guide to scientific writing in this list and the advice is reinforced with specific examples.

If you’ve ever written a critical literature review, you probably identified a handful of authors whose papers were just more enjoyable to read. This book can help you become that author. This book also makes for a great gift for a grad student about to dive into first-author writing.

Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded by Joshua Schimel

Great technical writing tells a story. If you’re wondering how experiments and data can be framed as a story, then this book is a must-read for you. This is one of the best books for writing fellowship proposals, research proposals and research grants. Dr. Schimel comes from a biology background but his experience on major government funding agency panels has given him the insight to know what gets funded.

I thoroughly enjoyed the way the author breaks down classical story structure and relates it to the segments of a strong research proposal. Don’t skip the exercises; they are the most valuable part of this book. He’s exceptionally good at exploring these ideas at all levels, from the macro to the micro, and I came away with a much clearer picture of how to write a cohesive and multi-level proposal.

It’s an easy weekend read that you should approach with a highlighter a notepad ready. If this one book helps you land even one grant, it will be paying itself back roughly 1000-fold.

The Literature Review: Six Steps to Success by Lawrence Machi

I took a chance on this book before writing my literature review for qualifiers. We eventually published the review to a major journal in my field and it’s gained over 600 citations in the first 4 years! There’s definitely some great advice in here that helped guide me toward writing a well-received paper.

Starting your literature review is the hardest part. It feels like a daunting task without a clear path to success. This book helps break down each step in the process into achievable goals supplemented by strategies for efficiently and effectively approaching each one. The few hours spent reading this book will be paid back to you in saving time researching and writing later.  It will help save your sanity and reduce anxiety approaching your first literature review.

I recommend this book specifically for graduate students in their first two years of a Masters’ or PhD. It can easily be read in an afternoon but should be used as a reference throughout the process!

Books to more clearly convey your research data to the reader

These three books below by Edward R. Tufte completely revolutionized my approach to creating graphs, figures and tables in both journal articles and conference presentations. I attended one of his full-day seminar courses around the country where Dr. Tufte works through the failures of that status quo in data presentation and showed gorgeous and enlightening examples of how good it can be.

I strongly believe this book series is the key reason why some of my journal articles have been so highly cited. Authors tend to cite papers that clearly convey a point and are more likely to reproduce figures that can stand on their own without wordy descriptions. See for yourself the difference these can make in your research career!

Beautiful Evidence by Edward R. Tufte

This book highlights innovative examples of data visualization spanning hand-drawn 17 th century charts to computer-generated “big data” presentation that will open your mind to forms of data visualization outside of your standard color-coded X-Y plots. The author also details strategies for identifying cherry-picked data and being a keen observer fraudulent data presentation.

This book is also the best gift for graduate students and post-docs on this list, making for a perfect coffee table book after fully reading through it.

Envisioning Information by Edward R. Tufte

This book walks the reader through a huge range of first-class graphical data representations and shows how each is well-suited to presenting the data at hand. Dr. Tufte makes you think about how different data types are structured and how those structures can guide you to the best methods of presentation.

The data visualization here is often layered so that your first glance gets the main point across but a closer examination unveils rich multi-dimensional data by cleverly using colors, shapes, sizes and alignments of objects and axes. These are the skills that create an ultimate, self-supporting figure for a journal cover or a winning poster that will hang for years on the walls of your institution without needing you there to explain it.

The Visual Display of Quantitative Information by Edward R. Tufte

A timeless classic on data visualization that dives into the nitty gritty of optimizing your charts and figures. Tufte contrasts excellent charts with horrendous ones to point out bad habits that you may not know you have. He teaches you about efficient design and layout of plots, from the ratio of ink that makes up your data to how one should effectively use tick marks on the axes or box plots around your data groupings.

This book may at first seem outdated, but the principles inside do not change. If you want to truly master the art of effective data visualization, this book can’t be skipped over.

Books to increase your productivity and focus in grad school:

The miracle morning.

I’m not exaggerating when I say this book thoroughly changed my life. I reached a point where I was physically and mentally exhausted halfway through each work day and realized I was not cognitively performing at the level I needed to be successful. Within a few days of implementing this, I felt a noticeable change in my energy, mood and motivation each day at work. I only wish I had picked it up in graduate school. My research output probably would have doubled simply from the changes to my mood.

The premise is fairly simple. Hal Elrod was recovering from a near-fatal car accident that left him physically and mentally impaired. He took the six most popular morning routine practices (exercise, reading, journaling, visualization, affirmations and meditation) and started doing all of them every single morning before starting work or any other responsibilities. Over time, he refined the timing and intentions around each practice and started sharing it with friends. It eventually exploded by word-of-mouth and he decided to write this book to share the technique with the world.

Yes, you’ll have to wake up a little earlier. Ideally you set aside one hour to do all six practices but with practice you can get most of the beneficial effects in less than 15 minutes. The book isn’t completely necessary to implement this – you can read enough about it online. But by all accounts, you’ll have a much higher chance of follow-through if you purchase and read the book as I did.

I started the practice a few months ago and used my “reading” time to read this book a few pages per day. Starting each morning with this book was essential to helping me refine the other five following practices and approach them with intention for maximum benefit.

Most days I squeeze in all six practices. Some days it’s only four and on some weekends only one or two. The key is to keep trying and don’t miss on two days in a row. The extra time spent in the morning comes back to me in productivity and focus throughout the day.

I can’t recommend this book enough for anyone whose workday is self-driven and self-structured like a typical grad student research life. Read it sooner rather than later and witness the profound effects it can have on every aspect of your life!

The Bullet Journal Method: Track the Past, Order the Present, Design the Future by Ryder Carroll

Have you heard of Bullet Journaling? It’s a method invented by Ryder Carroll to design your life and live intentionally that in a few short years has spawned a global movement and thriving community. It helps cut through the unnecessary “busy” tasks to focus on what matters. The technique can be done in any standard notebook but involves quite a bit of symbols and shorthand one must learn to truly gain the full benefit.

This book is the comprehensive how-to guide recently written by Ryder. For added effect, he includes how this method can help to de-clutter your life and bring you greater peace of mind. If you’re a “BuJo” newbie, this book will take you from novice to professional in a few weeks of practice.

The technique can have a profound effect on productivity and design of your research tasks to cut through to results you really need. Research has so many moving parts from experiments to data analysis to writing and publishing that this method is incredibly well-suited to keep track of. You might want to pair the book with this symbol stencil and journal bookmark if you’re not already familiar with the technique.

Fun reads for any scientist or engineer:

Skunk works: a personal memoir of my years at lockheed by ben rich.

A popular and highly-rated classic about the top secret “Skunk Works” engineering projects at Lockheed Martin that helped win the Cold War, written by the head of the division for two decades. It covers the pinnacle of high-pressure, high-stakes ultra-secretive engineering projects and the technological game of chess that the USA was playing with the Soviet Union in the 1970’s and 1980’s. It includes anecdotes and testimonials from high-ranking government officials and pilots on revolutionary projects like the SR-71 Blackbird, F-116 Stealth Fighter and U-2 spy plane.

This book is an enjoyable and inspiring read for any grad student who has a true passion for problem solving and cutting-edge technology. The reader will also take away valuable lessons for managing technical projects and teams of scientists and engineers to achieve nearly impossible goals.

Check the price on Amazon

The Martian by Andy Weir

This is our all-time favorite book that any scientist or engineer will enjoy reading. For such a technical book, it’s got an incredible plot yet isn’t overly dramatized (except a little at the end). No other fiction book has captured this much popularity while running through exact calculations, estimations and scientific principles just to keep someone alive. You’ll be rooting for Mark Watney and inspired by the idea that your technical knowledge could one day save your life.

Looking for gift ideas for a grad student or researcher?

We've further curated several collections of our own inventions depending on the type of researcher you're looking for. See these more niche collections below for more ideas!

9 Unique Gift Ideas for Scientists

Gifts for Professors and Grad School PIs

Unique Gifts for Graduate and PhD Students

Gifts for Chemists and Chemistry Students

Gifts for Scientists and Engineers

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