phd economics books

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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

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.

• What Is Mathematical Economics?
• Why Get an Economics Ph.D?
• What You Should Know Before Applying to an Economics PhD Program
• Should I Earn an Economics Degree?
• Choosing the Best Economics Graduate Program
• Ace Your Econometrics Test
• What Are the Various Subfields of Economics?
• Timeline for Applying to Graduate School
• Choosing an Ivy League Business School
• What is Grad School Like?
• Study Tips for the GRE Vocabulary Section
• Should You Apply to Graduate School With a Low GPA?
• Microeconomics Vs. Macroeconomics
• 6 Tips Applying to Grad School for a Different Major
• Applying to Graduate School: What You Need to Know

Textbooks are primarily used in the first year of a PhD program. Second-year readings are typically journal articles.

Publication year given is that for the last print or reprint of the most up-to-date edition. For books marked with a ☼, (legal) international editions are sold in some countries (e.g. Singapore), often at a fraction of the U.S. price.

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Econometrics

• Bruce Hansen

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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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Getting a PhD in Economics

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Considering a graduate degree in economics? Good choice: the twenty-first-century financial crisis and recession have underscored the relevance of experts who know how the economy works, should work, and could work. However, Ph.D. programs in economics are extremely competitive, with a high rate of attrition and a median time of seven years to completion. Also, economic professions come in many shapes and sizes, and while a doctoral degree is crucial training for some, it is less beneficial for others. How do you know whether a Ph.D. in economics is for you? How do you choose the right program—and how do you get the right program to choose you? And once you've survived years of rigorous and specialized training, how do you turn your degree into a lifelong career and meaningful vocation? Getting a Ph.D. in Economics is the first manual designed to meet the specific needs of aspiring and matriculating graduate students of economics. With the perspective of a veteran, Stuart J. Hillmon walks the reader though the entire experience—from the Ph.D. admissions process to arduous first-year coursework and qualifying exams to armoring up for the volatile job market. Hillmon identifies the pitfalls at each stage and offers no-holds-barred advice on how to navigate them. Honest, hard-hitting, and at times hilarious, this insider insight will equip students and prospective students with the tools to make the most of their graduate experience and to give them an edge in an increasingly competitive field.

• ISBN-10 0812222881
• ISBN-13 978-0812222883
• Publisher University of Pennsylvania Press
• Publication date February 13, 2014
• Language English
• Dimensions 5.75 x 0.75 x 8.75 inches
• Print length 152 pages
• See all details

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Book description, about the author, excerpt. © reprinted by permission. all rights reserved..

Chapter 1 Preliminaries: The Lowdown on Academic Economics and Ph.D. Programs So you're thinking of going to graduate school in economics. I applaud your good taste and discernment. Now is the right time to study economics. Thanks to Freakonomics and blog- and op-ed-wielding economists, we Ph.D. economists seem almost cool; not only can we analyze the stock market, we know something about sumo wrestling. And more of us economists are wanted and needed. The financial crisis of 2008 and the Great Recession have made it abundantly clear how important it is to have people around who know and understand what's going on in the economy. But there are many misconceptions about economics and about graduate economics training. The purpose of this book is to introduce you to the world of academic economics by way of a guide through a Ph.D. program in economics. My goal is to have you come out with a clear-eyed view of what is required to become an academic (research) economist, and to equip you with the required tools. But before we get started on your graduate school adventure, we need to take a step back: we need to double-check your sanity. By this I mean we need to make sure you are clear on the (real) purpose of a Ph.D. program in economics and on what exactly it is that this program is meant to do. Graduate school is not all rainbows and unicorns, but it has a chance of coming pretty close if you understand why you're there. The (Real) Purpose of a Ph.D. Program in Economics To start with the obvious, Ph.D. programs are unlike any other graduate program. In particular, a distinction is always made between Ph.D. programs and "professional" graduate programs like law school, medical school, or business school. These professional programs are intended to train you in a profession—namely, law, medicine, and business. Ph.D. programs, in contrast, are all about the life of the mind and scholarship and thinking heavy thoughts, and indeed, a good Ph.D. program includes all of these things. But Ph.D. programs, even if they aren't so-named, are also professional programs. You should never forget that the purpose of a Ph.D. program in economics is to train you in the profession of economic research. Put differently, a Ph.D. program in economics is meant to train you to become a research professor in economics. It is not meant to train you to be a quant jock on Wall Street or a policy maker in Washington. That's right, the purpose of Ph.D. programs in economics is to produce research professors in economics; there is no other purpose. Of course, many students who graduate with Ph.D.s in economics go on to do other interesting and important things like working on Wall Street or making public policy, but few programs and even fewer faculty would say that these other things are what economics programs are designed to train students to do. Many students who enter Ph.D. programs are largely unaware of this primary purpose. Yet, if you come into these programs without having adopted this very purpose for yourself, you may well be surprised if not downright miserable. Here's an example that is more concrete. Suppose you decided to go to law school, not because you wanted to be a lawyer, but because you thought that law school might be useful for, say, starting your own car repair shop. Repair shop owners occasionally get sued, so it would be useful for a car repair shop owner to know something about law. What would your experience of law school be like? You would be unhappy with professors who were teaching you constitutional law and criminal procedure, which have nothing to do with your interests. You would be unhappy with fellow students who talked about case this and precedent that all the time. You would be crying over the boring cases you were forced to study. By and large, you would be miserable and would find the curriculum, faculty, and students intolerably narrow. While you might find a course or two useful for the car repair business, law school is primarily meant to train lawyers, not entrepreneurial auto mechanics. Likewise, if you go into a Ph.D. program in economics to do something other than to become a research professor in economics, you will be deeply unhappy. If you are, however, clear from the start about what the real purpose of this program is, there is some chance that you will benefit a great deal from the program and even enjoy it. What Academic Economists Do Let's make a distinction between what Ph.D. economists do and what academic or research Ph.D. economists—i.e., economists with Ph.D.s sitting in universities or research institutes—do. You may see economists with Ph.D. degrees doing lots of interesting things. They advise presidents; they consult for firms, banks, and investment companies; they study economic trends; they analyze public policy and make policy recommendations; they teach undergraduate and graduate students. Academic economists do fewer interesting things. They can act as advisors and consultants, but that is not their primary job. They may be expected to teach and advise students but in many places that is not their main job either. The main job of an academic economist is to write research articles and publish them. Writing and publishing articles is not easy. It requires understanding the existing academic economics literature, contributing in original ways to it, and convincing other academic economists that you've just done something interesting. This summary is a highly abbreviated version of academic economics but it is pretty accurate. A crucial missing piece of information, though, is this mysterious thing called "the academic economics literature" that economists are supposed to know and contribute to. These days, this literature is highly mathematical and can be very abstract. Much of the literature has relevance to the real world, but the applications may not be obvious to those unfamiliar with the current jargon and methods of academic economics. This is where the Ph.D. program comes in. The first two years of a Ph.D. program in economics are designed to teach you what the profession considers to be the most important aspects of the current economics literature. The rest of the program is designed to train you to be able to contribute to this literature. Bad and Good Reasons for Doing a Ph.D. in Economics There are perhaps two good reasons to study for a Ph.D. in economics and about 3,007 bad reasons. Here are the top four bad yet unnervingly common reasons that students enter Ph.D. programs in economics: You're generally smart and did well in school but you don't know what to do next with your life. You can't find a job, and it's a good holding pattern until you can get one (plus, you can get a master's degree in economics, which is as good as an M.B.A.). You want to make lots of money. You want to work in public policy and save the world. Let's unpack each of these and see why they won't work so well. 1. You're generally smart and did well in school but you don't know what to do next with your life. While professors in the academy are all for having smart graduate students who did well in school, Ph.D. programs are a terrible place to be if you aren't quite sure what to do next. The main reason for this is because Ph.D. programs are narrower in their focus than almost any other graduate program that you might consider. You will surely get an immersion baptism in graduate economics, but this turns out to be much, much narrower than you might think based on your undergraduate economics classes. If you are unsure of what you want to do next, a brutal first year (and all economics programs are brutal in their first year) in a specialized field will surely not help you explore or find things you want to do. In fact, a bad first year can jeopardize your chances of ever doing economics again, should you want to pick it up later, because fewer programs will take a chance on you. So if you don't know what to do, going to a Ph.D. program in economics will definitely not help you know where to go and it might even close off future prospects. 2. You can't find a job, and it's a good holding pattern until you can get one (plus, you can get a master's degree in economics, which is as good as an M.B.A.). Unless you have very screwy preferences, it is not clear that being forced to study a difficult, technical, narrow subject is better than being unemployed. You will get paid beans during grad school (if you're lucky), and you will certainly not have time to sleep, much less go job-hunting, while you're in grad school. And we should dispense here and now with this fallacy that a master's degree in economics is as good as an M.B.A. While there is practical utility to be gained from M.B.A. coursework, much of the benefit of an M.B.A. program is in getting to know your classmates—working professionals who may be helpful in your business career—and in the career services offered to M.B.A. students. So, if you get a master's in economics, not only will you be forced to take courses that are much more technically demanding, time consuming, and less relevant for the business world, you will also extract none of the most important benefits of an M.B.A. program. And the average business employer is much more interested in hiring an M.B.A. graduate—a quantity she knows—than a master's in economics graduate. 3. You want to make lots of money. Snort. Not sure this deserves comment. Obviously, if you want to make lots of money, you should get an M.B.A. instead of a Ph.D. in economics; this fact has been empirically confirmed many times over. 4. You want to work in public policy and save the world. While this is more feasible than reason number 3, getting a Ph.D. in economics is an awful lot of work to prepare for a job in public policy. Much policy work that has any real-world relevance requires very little of the abstruse mathematical juggling that is the bread and butter of economics programs. Further, economics programs won't teach you anything about saving the world, and you probably won't save the world either. Although many economists do act as policy advisors, getting a Ph.D. in economics is a more difficult path than alternative paths that can also lead to working in policy and saving the world. Those are four bad reasons for wanting to do a Ph.D. in economics. Now here are two good reasons: You want to do economic research, you have questions you're interested in answering, and you have ideas about how to answer them. You want to teach economics at the university level. Some people might even argue that reason number 1 is the only good reason for doing a Ph.D. in economics. I include teaching as reason number 2 because: (1) it is an empirical reality that many colleges and universities will only hire Ph.D. graduates for their teaching positions; and (2) almost all academic economics positions require you to both teach and do research, even if the pay and promotion incentives are based on your research. To the extent that you think you might enjoy teaching economics at the university level as well as conducting economic research, these are the two best reasons to get a Ph.D. in economics. Going to Grad School Now Suppose this all sounds good to you, but you're not quite sure if now is the right time. Perhaps you did blindingly well in college, but you're feeling a little burnt out and need a break. Or you are tired of being poor and you would like to spend a couple of years making some serious money. In general, if you have other things that you want to do with your life right now (make money, travel the world, play in a band), you should go and do them. Grad school will still be here. Once you start on the econ grad school track, it will be all-consuming and you should be intellectually and personally prepared for the challenge. It's not easy to take a break during grad school and get back on track. If you're not 100 percent sure that you're ready right now, the time away from school will scratch that (non-economic) itch and might make you even more keen to come back. Indeed, many students who jump into grad school without giving much thought to other things they might want to do regret not having pursued these other options before grad school. Now, you may be concerned that if you don't go to grad school right now, your skills will deteriorate. It is true if you spend time away from doing economics and math, you will be a little creaky when you come back. As long as your time away is not more than two or three years, though, it will not take too long for you to recall the things you learned and get up to speed, so this should not be a particular worry. The most important thing is that you have taken the time to fulfill any other ambitions you might have. Grad school is like marriage, a long-term relationship requiring a serious commitment; it's probably a good idea to date around a little before you get married. Economics Versus Friends of Economics Students often wonder if they really want to be in a Ph.D. program in economics, as opposed to a program in a closely related field. These days, you can get a Ph.D. in public policy, finance, business economics, political economy, or health economics. Why not one of those? A Ph.D. program in economics is not the right choice for everyone, but before deciding whether to devote yourself to economics or to a field friendly to economics (hereafter referred to as FOE, friend of economics), you should know that economics and FOE degrees are not easily substitutable. There are obvious differences in curriculum, and related to that and perhaps more important, there are differences in job opportunities. While economics Ph.D. graduates can and do find employment in economics departments, business schools, political science departments, and health policy departments in medical schools, Ph.D. graduates of these FOE programs are typically employable only in FOE departments even if they are working on exactly the same topic that an economics graduate is . Unfair but true. What's more, in certain FOE departments, economics graduates may still have an advantage over FOE graduates; that is, an economics graduate may have a better chance of being employed at some public policy schools than a public policy graduate. Thus, economics graduates generally have a wider range of employment opportunities than FOE graduates. If you are unsure of your area of interest within economics or if you are keen to have the broader training afforded by economics, an econ program would be a better option for you. If you are very sure of your research interests and don't want to waste your time taking courses not related to them, a FOE program is a better choice. In some programs, the courses and qualifying exams you take are identical to those required of economics students, but you take fewer of them. For example, business economics students may be required to take the same microeconomics and econometrics courses as the economics students, but are excused from macroeconomics courses. You should also know that, not only do FOE programs have a narrower focus, they also tend to have a more applied focus, and this is reflected in the content of the courses and in faculty research interests. Having noted the differences between the two types of programs, I should of course mention that despite these differences, the advice in this book is helpful for Ph.D. FOE students as well as econ students. Notwithstanding the curricular and labor market differences, the structure of FOE programs is very similar to that of economics programs, and FOE students and economics students face many of the same academic and professional issues. Master's Degrees If you are interested in a Ph.D. program in economics, should you get a master's degree first? Perhaps you're unsure of whether you really would like to commit to a Ph.D. program and just want to dip your toes in the water. Or your undergraduate degree was not in economics and you're thinking about doing a Ph.D. in economics. In the United States, the master's degree is usually conferred as doctoral students progress through the program; there are few terminal master's programs in economics. This is changing somewhat as more mid-level departments are offering the master's degree option; in addition, a few respected economics departments have in recent years begun offering terminal master's degrees (for example, Boston University, Duke, and New York University). There are also good master's degree programs overseas. Many top universities in Europe (e.g., Cambridge, London School of Economics, Oxford, and Universitat Pompeu Fabra in Spain) offer terminal master's programs and confer degrees that are well-respected in the United States. Bear in mind that these programs will be demanding and technical. They consist of the first year of coursework in a Ph.D. program and cover perhaps the least fun part of any graduate program. If you want to dip your toes in the graduate economics water, a master's program is not a bad idea. It will help you get your skill set together, help you find out if you enjoy graduate study in economics, and show others that you are serious about economics. If you do go on for your Ph.D., your experience during the first year of your Ph.D. will be much more pleasant because you will have seen a lot of the same material in your master's program. There are some downsides, however. In many master's programs, and especially the overseas master's programs, there is very little funding so you will have to fund yourself. Second, in many European programs, exams take place only once a year. American students have difficulty knowing how to study for these kinds of exams and good students can do poorly on them; a whole year's worth of work and tuition, and your Ph.D. admission prospects, may go down the tubes if you have a bad day or two so it's a bit of a risk. By the way, you should also know that a master's degree in economics has little professional value beyond getting you into a better Ph.D. economics program than you would have otherwise gotten into (although of course it has intellectual value). As I mentioned above, it is a poor substitute for an M.B.A. or, say, a master's in public policy. So if you decide to go for your master's degree, do it with the understanding that its only value will be to help you prepare for your Ph.D. A Few Factoids About Economics Ph.D. Programs So much for the highly opinionated part of this chapter. Now for a few facts. You may be curious to know some general statistics about economics Ph.D. programs. I should note that, here and throughout the book, I will focus primarily on economics training in the United States. Although the economics profession has become much more globalized in recent decades and there are many excellent economists and doctoral economics programs overseas, most advanced economics training still occurs in U.S. programs. (There is also more heterogeneity in the structure of non-U.S. training programs compared to U.S. programs.) My U.S. focus should be interpreted simply as reflecting the system I know best and as an attempt at addressing the needs of the majority of economics students, rather than as a dismissal of non-U.S. alternatives. According to the American Economic Association (AEA), there are 136 Ph.D. programs in economics in the United States. There are no official estimates of the total number of students in these programs, but several back-of-the-envelope calculations suggest the number is around 10,000. The top fifteen programs tend to have more students than the lower-ranked programs; an estimate from the 1980s suggests that program enrollment in the top fifteen programs averages around 130 students, whereas enrollment in lower-ranked program averages around 50-70 students (Hansen 1991). Since the 1970s, there have been steady increases in the percentage of doctorates in economics awarded to women. In 1977, about 9 percent of economics doctorates were awarded to women; in 1987, this proportion was 19 percent; and in 2001, it was 28 percent (Siegfried and Stock 2004). There have also been increases in the percentage of economics doctorates awarded to non-U.S. citizens. In 1977, about 33 percent of economics doctorates were awarded to non-U.S. citizens; by 2001, this percentage had increased to 62 percent (Siegfried and Stock 2004). And finally one last statistic: how long will take you to finish? Had you had the foresight and fetal know-how to graduate in 1977, the median time to completion would have been 5.7 years. Time to completion has been steadily increasing, and in 2001, the median time to Ph.D. was 7 years (Siegfried and Stock 2004). If these and other fun cocktail-party facts interest you, you can find more in the papers listed in the American Economic Association's extensive bibliography of studies on graduate economics education (URL below). For now, I'm presenting you with a few facts about economics Ph.D. programs because it's always good to know about the institution to which you are committing yourself. But overall statistics can only say so much. Mostly my job is to inform you about the experience of being a graduate student. Still here? Consider yourself briefed on economics Ph.D. programs and their real purpose (to produce professors). If after reading this chapter, you are still keen and are ready to make a serious commitment to such a program, read on.

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• Publisher ‏ : ‎ University of Pennsylvania Press (February 13, 2014)
• Language ‏ : ‎ English
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• ISBN-13 ‏ : ‎ 978-0812222883
• Item Weight ‏ : ‎ 7.6 ounces
• Dimensions ‏ : ‎ 5.75 x 0.75 x 8.75 inches
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The complete guide to getting into an economics PhD program

Back in May, Noah wrote about the amazingly good deal that is the PhD in economics. Why? Because:

• You get a job.
• You get autonomy.
• You get intellectual fulfillment.
• The risk is low.
• Unlike an MBA, law, or medical degree, you don’t have to worry about paying the sticker price for an econ PhD:  After the first year, most schools will give you teaching assistant positions that will pay for the next several years of graduate study, and some schools will take care of your tuition and expenses even in the first year. (See Miles’s companion post  for more about costs of graduate study and how econ PhD’s future earnings makes it worthwhile, even if you can’t get a full ride.)

Of course, such a good deal won’t last long now that the story is out, so you need to act fast! Since he wrote his post , Noah has received a large number of emails asking the obvious follow-up question: “How do I get into an econ PhD program?” And Miles has been asked the same thing many times by undergraduates and other students at the University of Michigan. So here, we present together our guide for how to break into the academic Elysium called Econ PhD Land:

(Note: This guide is mainly directed toward native English speakers, or those from countries whose graduate students are typically fluent in English, such as India and most European countries. Almost all highly-ranked graduate programs teach economics in English, and we find that students learn the subtle non-mathematical skills in economics better if English is second nature. If your nationality will make admissions committees wonder about your English skills, you can either get your bachelor’s degree at a—possibly foreign—college or university where almost all classes are taught in English, or you will have to compensate by being better on other dimensions. On the bright side, if you are a native English speaker, or from a country whose graduate students are typically fluent in English, you are already ahead in your quest to get into an economics PhD.)

Here is the not-very-surprising list of things that will help you get into a good econ PhD program:

• good grades, especially in whatever math and economics classes you take,
• a good score on the math GRE,
• some math classes and a statistics class on your transcript,
• research experience, and definitely at least one letter of recommendation from a researcher,
• a demonstrable interest in the field of economics.

Chances are, if you’re asking for advice, you probably feel unprepared in one of two ways. Either you don’t have a sterling math background, or you have quantitative skills but are new to the field of econ. Fortunately, we have advice for both types of applicant.

If you’re weak in math…

Fortunately, if you’re weak in math, we have good news:  Math is something you can learn . That may sound like a crazy claim to most Americans, who are raised to believe that math ability is in the genes. It may even sound like arrogance coming from two people who have never had to struggle with math. But we’ve both taught people math for many years, and we really believe that it’s true. Genes help a bit, but math is like a foreign language or a sport: effort will result in skill.

Here are the math classes you absolutely should take to get into a good econ program:

• Linear algebra
• Multivariable calculus

Here are the classes you should take, but can probably get away with studying on your own:

• Ordinary differential equations
• Real analysis

Linear algebra (matrices, vectors, and all that) is something that you’ll use all the time in econ, especially when doing work on a computer. Multivariable calculus also will be used a lot. And stats of course is absolutely key to almost everything economists do. Differential equations are something you will use once in a while. And real analysis—by far the hardest subject of the five—is something that you will probably never use in real econ research, but which the economics field has decided to use as a sort of general intelligence signaling device.

If you took some math classes but didn’t do very well, don’t worry.  Retake the classes . If you are worried about how that will look on your transcript, take the class the first time “off the books” at a different college (many community colleges have calculus classes) or online. Or if you have already gotten a bad grade, take it a second time off the books and then a third time for your transcript. If you work hard, every time you take the class you’ll do better. You will learn the math and be able to prove it by the grade you get. Not only will this help you get into an econ PhD program, once you get in, you’ll breeze through parts of grad school that would otherwise be agony.

Here’s another useful tip:  Get a book and study math on your own before taking the corresponding class for a grade. Reading math on your own is something you’re going to have to get used to doing in grad school anyway (especially during your dissertation!), so it’s good to get used to it now. Beyond course-related books, you can either pick up a subject-specific book (Miles learned much of his math from studying books in the Schaum’s outline series ), or get a “math for economists” book; regarding the latter, Miles recommends Mathematics for Economists  by Simon and Blume, while Noah swears by Mathematical Methods and Models for Economists  by de la Fuente. When you study on your own, the most important thing is to  work through a bunch of problems . That will give you practice for test-taking, and will be more interesting than just reading through derivations.

This will take some time, of course. That’s OK. That’s what summer is for (right?). If you’re late in your college career, you can always take a fifth year, do a gap year, etc.

When you get to grad school, you will have to take an intensive math course called “math camp” that will take up a good part of your summer. For how to get through math camp itself, see this guide by Jérémie Cohen-Setton .

One more piece of advice for the math-challenged:  Be a research assistant on something non-mathy . There are lots of economists doing relatively simple empirical work that requires only some basic statistics knowledge and the ability to use software like Stata. There are more and more experimental economists around, who are always looking for research assistants. Go find a prof and get involved! (If you are still in high school or otherwise haven’t yet chosen a college, you might want to choose one where some of the professors do experiments and so need research assistants—something that is easy to figure out by studying professors’ websites carefully, or by asking about it when you visit the college.)

If you’re new to econ…

If you’re a disillusioned physicist, a bored biostatistician, or a neuroscientist looking to escape that evil  Principal Investigator, don’t worry:  An econ background is not necessary . A lot of the best economists started out in other fields, while a lot of undergrad econ majors are headed for MBAs or jobs in banks. Econ PhD programs know this. They will probably not mind if you have never taken an econ class.

That said, you may still want to  take an econ class , just to verify that you actually like the subject, to start thinking about econ, and to prepare yourself for the concepts you’ll encounter. If you feel like doing this, you can probably skip Econ 101 and 102, and head straight for an Intermediate Micro or Intermediate Macro class.

Another good thing is to  read through an econ textbook . Although economics at the PhD level is mostly about the math and statistics and computer modeling (hopefully getting back to the real world somewhere along the way when you do your own research), you may also want to get the flavor of the less mathy parts of economics from one of the well-written lower-level textbooks (either one by Paul Krugman and Robin Wells , Greg Mankiw , or Tyler Cowen and Alex Tabarrok ) and maybe one at a bit higher level as well, such as David Weil’s excellent book on economic growth ) or Varian’s Intermediate Microeconomics .

Remember to take a statistics class , if you haven’t already. Some technical fields don’t require statistics, so you may have missed this one. But to econ PhD programs, this will be a gaping hole in your resume. Go take stats!

One more thing you can do is research with an economist . Fortunately, economists are generally extremely welcoming to undergrad RAs from outside econ, who often bring extra skills. You’ll get great experience working with data if you don’t have it already. It’ll help you come up with some research ideas to put in your application essays. And of course you’ll get another all-important letter of recommendation.

And now for…

General tips for everyone

Here is the most important tip for everyone:  Don’t just apply to “top” schools . For some degrees—an MBA for example—people question whether it’s worthwhile to go to a non-top school. But for econ departments, there’s no question. Both Miles and Noah have marveled at the number of smart people working at non-top schools. That includes some well-known bloggers, by the way—Tyler Cowen teaches at George Mason University (ranked 64th ), Mark Thoma teaches at the University of Oregon (ranked 56th ), and Scott Sumner teaches at Bentley, for example. Additionally, a flood of new international students is expanding the supply of quality students. That means that the number of high-quality schools is increasing; tomorrow’s top 20 will be like today’s top 10, and tomorrow’s top 100 will be like today’s top 50.

Apply to schools outside of the top 20—any school in the top 100 is worth considering, especially if it is strong in areas you are interested in. If your classmates aren’t as elite as you would like, that just means that you will get more attention from the professors, who almost all came out of top programs themselves. When Noah said in his earlier post that econ PhD students are virtually guaranteed to get jobs in an econ-related field, that applied to schools far down in the ranking. Everyone participates in the legendary centrally managed econ job market . Very few people ever fall through the cracks.

Next—and this should go without saying— don’t be afraid to retake the GRE . If you want to get into a top 10 school, you probably need a perfect or near-perfect score on the math portion of the GRE. For schools lower down the rankings, a good GRE math score is still important. Fortunately, the GRE math section is relatively simple to study for—there are only a finite number of topics covered, and with a little work you can “overlearn” all of them, so you can do them even under time pressure and when you are nervous. In any case, you can keep retaking the test until you get a good score (especially if the early tries are practice tests from the GRE prep books and prep software), and then you’re OK!

Here’s one thing that may surprise you: Getting an econ master’s degree alone won’t help . Although master’s degrees in economics are common among international students who apply to econ PhD programs, American applicants do just fine without a master’s degree on their record. If you want that extra diploma, realize that once you are in a PhD program, you will get a master’s degree automatically after two years. And if you end up dropping out of the PhD program, that master’s degree will be worth more than a stand-alone master’s would. The one reason to get a master’s degree is if it can help you remedy a big deficiency in your record, say not having taken enough math or stats classes, not having taken any econ classes, or not having been able to get anyone whose name admissions committees would recognize to write you a letter of recommendation.

For getting into grad school, much more valuable than a master’s is a stint as a research assistant in the Federal Reserve System or at a think tank —though these days, such positions can often be as hard to get into as a PhD program!

Finally—and if you’re reading this, chances are you’re already doing this— read some econ blogs . (See Miles’s speculations about the future of the econ blogosphere here .) Econ blogs are no substitute for econ classes, but they’re a great complement. Blogs are good for picking up the lingo of academic economists, and learning to think like an economist. Don’t be afraid to  write  a blog either, even if no one ever reads it (you don’t have to be writing at the same level as Evan Soltas or Yichuan Wang );  you can still put it on your CV, or just practice writing down your thoughts. And when you write your dissertation, and do research later on in your career, you are going to have to think for yourself outside the context of a class . One way to practice thinking critically is by critiquing others’ blog posts, at least in your head.

Anyway, if you want to have intellectual stimulation and good work-life balance, and a near-guarantee of a well-paying job in your field of interest, an econ PhD could be just the thing for you. Don’t be scared of the math and the jargon. We’d love to have you.

Update:  Miles’s colleague Jeff Smith at the University of Michigan amplifies many of the things we say on his blog.  For a  complete  guide, be sure to see what Jeff has to say, too.

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Must Read Books for Aspiring Economists

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Introductions to Economics

• Freakonomics , Steven D. Levitt and Stephen J. Dubner In Freakonomics , authors Steven D. Levitt and Stephen J. Dubner unpack how people respond to incentives by investigating seemingly non-economic subjects like how parents pick names for their children and even how the legalization of abortion could be responsible for a drop in crime. Provocative and entertaining, Freakonomics is a crash course in the populist application of economics.
• The Armchair Economist: Economics and Everyday Life , Steven E. Landsburg Steven Landsburg argues that economics can be boiled down to four words: people respond to incentives. The book gives readers a layman's introduction to economics through incentives and their implications, good and bad, and how all aspects of our life are influenced by them. The Armchair Economist is also a good introduction to the so-called “Chicago school” of economics, of which Landsburg is himself a member.
• Naked Economics: Undressing the Dismal Science , Charles Wheelan If you are looking for a refresher in economics or an introduction to the broad strokes of the subject, Naked Economics is for you. Charles Wheelan starts with one basic premise, that people seek to maximize their utility, and expands into a larger exploration of free market theory and its implications.
• Misbehaving: The Making of Behavioral Economics , Richard H. Thaler In Misbehaving , Richard Thaler examines how the “rational actor” assumption often used in economic theory misunderstands how humans think and act. The book focuses on how human misbehavior has consequences that appear no matter how large or small a decision appears to be. Misbehaving is an impressive account of the major development in behavioral economics over the last half-century.

Fundamental Economics Texts

• Capitalism and Freedom , Milton Friedman Milton Friedman's iconic work argues that economic freedom is essential to a free and liberal society. Published in 1962, many of Friedman's theories presented in Capitalism and Freedom have since been adopted worldwide. Friedman’s work ranks among the top 100 non-fiction books written in English in the last century, according to Time Magazine.
• The Wealth of Nations , Adam Smith One of the most essential economics texts, The Wealth of Nations forms the underpinning of much of modern economic theory. For many students of economics, Wealth of Nations is the first book assigned in class, but rereading this fundamental text can provide a deeper understanding of both the foundations of economics and its transformation over the last 300 years.
• The Affluent Society , John Kenneth Galbraith Affluent Society offers a contradictory view of the “conventional wisdom” underpinning modern economic theories and principles. John Galbraith argues in his iconic work the conventional wisdom of economics is fundamentally flawed because the theory of economics came into being in the late eighteenth and early nineteenth centuries and is poorly suited to explain the mechanisms of the affluent post-WW2 U.S. economy.

Field-Specific Economics Books

• The Bottom Billion: Why the Poorest Countries are Failing and What Can Be Done About It , Paul Collier If your interest is working as an economist in international development or with international aid agencies, The Bottom Billion is a must read. Paul Collier challenges the notion that the best way to help the poor in struggling economies is with a cash infusion. Rather, Collier argues, a more hands-on approach is the solution.
• The Travels of a T-Shirt in a Global Economy , Pietra Rivoli For a look at how globalization and free trade impact the cost of goods, look no further than The Travels of a T-Shirt in a Global Economy . Pietra Rivoli's book provides a way to see globalization, a deeply complicated issue, through the production of a single t-shirt. For economics students interested in working in the fields of international trade or monetary policy, Travels of a T-Shirt is a must-read.
• Plunder and Blunder: The Rise and Fall of the Bubble Economy , Dean Baker Plunder and Blunder offers a look at the causes of the housing bubble that precipitated the 2001 and 2007 market crashes. Dean Baker analyzes the causes and effects of the crash, why people ignored the signs of an oncoming economic disaster and what can be done to prevent future financial bubbles.

You can develop quite an impressive understanding of economics on your own, but if you want to develop professional skills that you can apply in the real world, American University can help. Our online Master’s in Economics with a specialization in applied economics gives you all the skills needed to make an impact as an economist.

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• Getting a PhD in Economics

In this Book

• Stuart J. Hillmon
• Published by: University of Pennsylvania Press

Considering a graduate degree in economics? Good choice: the twenty-first-century financial crisis and recession have underscored the relevance of experts who know how the economy works, should work, and could work. However, Ph.D. programs in economics are extremely competitive, with a high rate of attrition and a median time of seven years to completion. Also, economic professions come in many shapes and sizes, and while a doctoral degree is crucial training for some, it is less beneficial for others. How do you know whether a Ph.D. in economics is for you? How do you choose the right program—and how do you get the right program to choose you? And once you've survived years of rigorous and specialized training, how do you turn your degree into a lifelong career and meaningful vocation? Getting a Ph.D. in Economics is the first manual designed to meet the specific needs of aspiring and matriculating graduate students of economics. With the perspective of a veteran, Stuart J. Hillmon walks the reader though the entire experience—from the Ph.D. admissions process to arduous first-year coursework and qualifying exams to armoring up for the volatile job market. Hillmon identifies the pitfalls at each stage and offers no-holds-barred advice on how to navigate them. Honest, hard-hitting, and at times hilarious, this insider insight will equip students and prospective students with the tools to make the most of their graduate experience and to give them an edge in an increasingly competitive field.

Table of Contents

• Title Page, Copyright Page
• 1: Preliminaries: The Lowdown on Academic Economics and Ph.D. Programs
• 2 :Applying to Ph.D. Programs: It's Both What You Know and Who You Know
• 3 Getting Through First Year: Welcome to Boot Camp
• 4: Acing Second Year: Getting On with Graduate Life
• 5: Finding a Topic and an Advisor: Like Getting Married … to a Polygamist
• 6: Getting Distracted: TAing, RAing, and the Meaning of Life
• 7: Thrown In with the Sharks: Women and International Students
• 8: Getting a Job: Taking Your Show on the Road
• pp. 114-136
• 9: Conclusion: The Ph.D. Economist-at-Large
• pp. 137-142
• pp. 143-146

Additional Information

PhD in Economics Programme: Books

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Introduction

These are titles that may be relevant to the corresponding courses. Click on the titles to find out more details about the individual books. 

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References for ECON 601 - Microeconomics

References for ECON 602 - Macroeconomics I

References for ECON 611 - Econometrics I

References for ECON 712 - Labour Economics

Journal Article References for ECON 712 - Labour Economics

• Keane, M.P. (2011): Labour supply and taxes: a survey.
• Keane, M.P. and R. Rogerson. (2012): Micro and macro labour elasticities: a reassessment of conventional wisdom.

References for ECON 713 - Nonparametric Econometrics

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Ph.D. Program

Make an impact: The intellectual rigor from researchers associated with Yale Economics drives innovations in domestic and international policy.

• Requirements

Yale's Department of Economics offers a challenging and rigorous academic program, a distinguished and accessible faculty, and a friendly, supportive environment for study.

Our core teaching faculty of 66 is supported by a diverse group of visiting professors and graduate student teaching assistants, making it one of the largest economics departments in the United States with one of the highest teacher/student ratios for the 130 Ph.D. students in residence.

The Department of Economics also has close ties with professional schools in related fields, such as the Yale School of Management, the Yale School of the Environment, and the Yale School of Public Health, where many of its secondary faculty members teach. It also works with affiliated centers, including the Cowles Foundation for Research in Economics, the Economic Growth Center, and the newly created Tobin Center for Economic Policy . 

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Our Program

Yale's economics faculty embraces a broad range of research and teaching interests. Courses and seminars span a wide spectrum of economics, from dynamic structural models to field experiments. Our students apply econometric and data analytic methods to a variety of subjects in macroeconomics, labor economics and finance. Our courses examine critical economic policy issues, including antitrust and environmental regulation. Our focus is global, spanning the United States and developed economies to the developing nations of Latin America, Asia and Africa. Whatever your interest, our faculty is ready to guide you through a wide offering of more than a hundred regular courses, seminars or workshops, combined with individually tailored reading and research courses to best prepare you for your Ph.D. research and dissertation.

Our faculty is eclectic in methodologies and views of economics. There is no Yale dogma or school. You will acquire a critical perspective on the full range of approaches to macroeconomics. You will be well trained in neoclassical theory and in the theory of public choice, externalities and market failures. You will master the skills of sophisticated modern econometrics and understand pitfalls in its applications. You will gain respect for the power of contemporary mathematical models and also for history and for the insights of the great economists of the past.  

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Dec. 21, Wed. Fall Term ends, Winter Recess begins.

Dec. 22 Thurs. Date of December degree award

Jan. 12, Thurs. Add/drop period opens, 8:30 am

Full calendar

PhD Program

Year after year, our top-ranked PhD program sets the standard for graduate economics training across the country. Graduate students work closely with our world-class faculty to develop their own research and prepare to make impactful contributions to the field.

Our doctoral program enrolls 20-24 full-time students each year and students complete their degree in five to six years. Students undertake core coursework in microeconomic theory, macroeconomics, and econometrics, and are expected to complete two major and two minor fields in economics. Beyond the classroom, doctoral students work in close collaboration with faculty to develop their research capabilities, gaining hands-on experience in both theoretical and empirical projects.

How to apply

Students are admitted to the program once per year for entry in the fall. The online application opens on September 15 and closes on December 15.

Meet our students

Our PhD graduates go on to teach in leading economics departments, business schools, and schools of public policy, or pursue influential careers with organizations and businesses around the world. 

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Search by topic or author to browse books faculty have contributed to as editors, authors, or chapter authors.

Research Topics

Research center.

Princeton University Press

A Crash Course on Crises

Johns Hopkins University Press

Metrics that Matter: Counting What’s Really Important to College Students

W. W. Norton & Company

January 2023

A Random Walk Down Wall Street: The Best Investment Guide That Money Can Buy (50th Anniversary Edition)

October 2022

A Monetary and Fiscal History of the United States, 1961–2021

Financial Economics of Insurance

December 2021

The Economics of Sovereign Debt and Default

Endeavor Literary Press

August 2021

The Resilient Society

World Scientific

A Safe-Asset Perspective for an Integrated Policy Framework

The Handbook of China’s Financial System

Games of Strategy

American Psychological Association

Confronting Inequality: How Policies and Practices Shape Children’s Opportunities

London: CEPR Press

Women in Economics

September 2019

The Attention Deficit: Unintended Consequences of Digital Connectivity

W.W. Norton & Company

January 2019

A Random Walk Down Wallstreet

Banco de Portugal

Portuguese Economic Growth: A View on Structural Features, Blockages and Reforms

World Trade Evolution: Growth Productivity and Employment

Introduction to Econometrics

Basic Books

Advice and Dissent: Why America Suffers When Economics and Politics Collide

Oxford University Press

Oxford Handbook of Structural Transformation

Harvard University Press

The Historical Origins of Global Inequality

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The doctoral program in Economics at Harvard University is one of the leading programs in the world. Supported by a diverse group of faculty who are top researchers in their fields and fueled by a vast array of resources, the PhD program is structured to train and nurture students to become leading economists in academia, government agencies, the technology industry, finance and banking, and global policy organizations.

Harvard University and the Department of Economics are regularly ranked amongst the top programs in the world, and the consistency of success among our graduates is inspiring. We have educated several foreign heads of state, Nobel Prize Winners, Clark Medal Winners, MacArthur Fellowship Recipients - many of whom have returned to Harvard to offer their expertise and brilliance in shaping and nurturing our students.  Learn more about where we place our  graduates  and explore our  Program  to find out if a PhD in Economics is a good fit for you. 

Program Requirements

As a PhD student in the Economics program, students will spend the first two years in the program engaged in rigorous coursework designed to develop a foundational understanding of economics. In the following years, students transition to research under the guidance of strong faculty mentorship and participate in field workshops. In the final year, students conduct independent research and complete a dissertation.

The department of Economics at Harvard University is committed to seeking out and mentoring scholars who wish to pursue a rigorous and rewarding career in economic research. Our graduates are trailblazers in their fields and contribute to a diverse alumni community in both the academic and non-academic sectors. We invite you to learn more and apply to the PhD program in Economics. 

Financial Support

Students have access to a variety of funding and financial support opportunities.

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Learn more about financial support

Upcoming Events

Ec 3008 graduate student workshop in international economics, location: .

Max Alekseev (Harvard)... Read more about EC 3008 Graduate Student Workshop in International Economics

Seminar in Behavioral and Experimental Economics

Jeffrey Yang (Harvard University) "Similarity and Comparison Complexity" (with Cassidy Shubatt)... Read more about Seminar in Behavioral and Experimental Economics

EC 3011 Graduate Student Workshop in Financial Economics

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