Microeconometrics using Stata.

This article was downloaded by: [University of California, San Diego] On: 25 May 2013, At: 00:43 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Econometric Reviews Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/lecr20 Book Review: Microeconometrics: Methods and Applications and Microeconometrics Using Stata Patrick Bajari a & Thomas Youle a a Department of Economics, University of Minnesota, Minneapolis, Minnesota, USA Published online: 13 Oct 2011. To cite this article: Patrick Bajari & Thomas Youle (2012): Book Review: Microeconometrics: Methods and Applications and Microeconometrics Using Stata , Econometric Reviews, 31:1, 107-117 To link to this article: http://dx.doi.org/10.1080/07474938.2011.607090 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Econometric Reviews, 31(1):107–117, 2012 Copyright © Taylor & Francis Group, LLC ISSN: 0747-4938 print/1532-4168 online DOI: 10.1080/07474938.2011.607090 BOOK REVIEW: MICROECONOMETRICS: METHODS AND APPLICATIONS AND MICROECONOMETRICS USING STATA Patrick Bajari and Thomas Youle Department of Economics, University of Minnesota, Minneapolis, Minnesota, USA Cameron, Colin A., Trivedi, Pravin K. (2005) Microeconometrics: Methods and Applications. New York: Cambridge University Press. Cameron, Colin A., Trivedi, Pravin K. (2009) Microeconometrics using Stata. Texas: Stata Press. 1. OVERVIEW Microeconometrics: Methods and Applications (MMA for short) by A. Colin Cameron and Pravin K. Trivedi is an in-depth, textbook-style treatment of techniques that are commonly used in applied microeconomics. The companion text Microeconometrics Using Stata (MUS for short) by the same authors shows how to apply these techniques in the powerful statistics software package Stata. Both texts are appropriate for Ph.D. students already familiar with the first few chapters of an introductory text such as Greene (2008), Hayashi (2000), or Ruud (2000). Both books are organized around econometric topics. MMA has a broader coverage and depth of material than MUS, which instead focuses on having readers get their hands dirty with real data sets on the computer. Both provide useful discussions for applied economists. Students will also learn about different data types and how to load and manipulate them in Stata. This sort of practical knowledge is very useful for Ph.D. students making the transition to applied research. Two features make MMA and MUS useful additions to the applied microeconomist’s bookshelf. First, they have a broader coverage of topics and are more current than many other available texts. After a first-year econometrics course, Ph.D. students are often frustrated when Address correspondence to Patrick Bajari, Department of Economics, University of Minnesota, 7031 A 1925 4th St. S, Minneapolis, MN 55455, USA; E-mail: bajari@umn.edu D ow nl oa de d by [U niv ers ity of C ali fo rn ia, Sa n D ieg o] at 00 :43 25 M ay 20 13 108 Book Review they attempt to read journal articles or follow seminar presentations in applied microeconomics. Many of the methods commonly used by leading practitioners are omitted in their first-year texts. The broad and up-to-date coverage goes a long way towards filling in these gaps. These books are also useful for practitioners who wish to quickly get up to speed on particular methods in order to read recent research. Second, a standard first-year text frequently omits material that cannot be formally and completely developed given page constraints. Many widely used methods are left out of standard texts as a result. By comparison, Cameron and Trivedi succinctly summarize widely used methods even if they are too advanced to formally develop in the text. As a result, Ph.D. students at least have a frame of reference for topics that they are likely to encounter in their lives after graduate school. Furthermore, the text provides detailed discussions of sticky implementation issues that are sometimes hard to formalize, but that nevertheless are likely to be encountered in practice. A good example of this difference in style is their coverage of instrumental-variables (IV) estimators. While Cameron and Trivedi discuss the standard theory of IV included in many first-year texts, they also discuss a number of additional topics. The authors have a detailed discussion of the choice of instruments in estimating the returns to education. The choice of instruments is seldom without a bit of controversy in applied work. Many first-year texts do not have detailed discussions of the difficulties that arise when trying to find a good instrument. As a result, students may be unprepared for the reaction they will face when they first begin to use IV in their own applications. Cameron and Trivedi also discuss the advantages and disadvantages of different IV estimators, such as the Jackknife IV and Limited Information ML. While the full comparison of these estimators requires advanced theory, the authors show they are easy to implement and compare in Stata. The relevant literature is cited for those who wish to investigate the formal econometric theory. Cameron and Trivedi also discuss certain theoretical pitfalls in applying IV, such as the weak instruments problem. Most first-year texts omit this topic since the relevant econometric theory is too advanced. As a result, students may be puzzled when they are asked to report their first-stage F-statistic when presenting their own research. Cameron and Trivedi, by comparison, provide an intuitive explanation of the weak instruments problem, discuss several alternative diagnostic tests for weak instruments, and then show how they can be implemented in Stata. Cameron and Trivedi provide a detailed discussion of research by Kling (2001) on estimating the returns to schooling. They compare the IV to the Ordinary Least Squares (OLS) estimates and discuss weak instruments in the context of this detailed application. This additional material teaches D ow nl oa de d by [U niv ers ity of C ali fo rn ia, Sa n D ieg o] at 00 :43 25 M ay 20 13 Book Review 109 students how to critically read papers and helps them in preparing their own papers for submission to peer-reviewed journals. Unfortunately, such references to the applied literature are casual and scarce. Examples tend to be chosen in order to clearly communicate the econometric properties of a method, but they seldom help familiarize students with a major applied literature. This is not a specific criticism of Cameron and Trivedi or of any particular first-year textbook. Instead, this suggests the need for a supplementary text which focuses on applications, which we will describe in the next section. The bottom line is that Cameron and Trivedi have provided an extremely valuable service to the profession by producing such detailed and comprehensive books. 2. SOME LIMITATIONS OF AVAILABLE ECONOMETRIC TEXTS As an instructor, we have found that there are two important gaps in the available graduate econometrics texts. First, the texts tend to underexpose students to substantive applications that occur in the major empirical literatures. Second, they draw few links between what students learn in their econometrics courses and what they learn in their economic courses. While a broad awareness of methods common in microeconometrics is a necessary component in the training of an applied microeconomist, it is not sufficient. Students must be able to think critically about both the economic and econometric issues which occur in applied work. Without this training, students find it difficult to make the transition to writing substantive empirical applications. An effective way to teach students how to do research is to expose them to many different empirical literatures. Students are then shown how econometrics can be used to attack a diverse array of problems in different subfields of economics. The classic Berndt (1996) follows such an approach. Each chapter is organized around a large applied literature such as those studying the capital asset-pricing model (CAPM), costs and learning curves, and the demand for electricity. In each chapter the relevant economic theory is discussed along with empirical facts, econometrics, and references to important papers. In a chapter on wage regressions, Berndt (1996) cites 164 papers (108 within 15 years of its publication) in the course of discussing human capital theory, signaling theory, econometrics, and recent research. In developing the wage regressions motivated by theory, Berdnt discusses the econometric issues of specifying a functional form, adding dummy variables for gender, and trying to control for the omitted variable bias resulting from unobserved ability. Students see that a broad understanding of economics is indeed useful in conducting applied research. Both professors and Ph.D. students would benefit from a 21st century equivalent of Berndt (1996) to supplement the primary text in an D ow nl oa de d by [U niv ers ity of C ali fo rn ia, Sa n D ieg o] at 00 :43 25 M ay 20 13 110 Book Review econometrics course. Ideally, each chapter would focus on a major empirical literature such as hedonic home-price regressions applied to estimating the value of environmental amenities, the estimation of production functions and productivity, or differentiated product demand applied to measuring market power. A given chapter would also contain one or two data sets used in prominent papers written by a leading researcher within the past 15 years. Finally, the text should have detailed problem sets with applications that force students to apply their econometric theory in Stata. Such a supplementary text would be invaluable in teaching students how economic theory and econometrics can be used together to explore applied problems. These comments are not criticisms of Cameron and Trivedi’s excellent work. There is no reason to expand the scope of their texts since each organized around a self-contained theme. However, a text that had substantive empirical applications that cover major empirical literatures would be invaluable as a supplement to standard econometrics texts. 3. HIGHLIGHTS OF MMA Here we discuss some highlights of MMA. When detailed chapter outlines are available on the internet, there is no need to simply repeat the table of contents. 3.1. Part 1: Preliminaries Although some of these topics are also covered in Wooldridge (2002), this section of MMA is unique to microeconometric textbooks because it provides an overview of how microeconometrics fits into applied microeconomics and how microeconometrics differs from other areas of statistics. The discussion in Chapter 2 on causal and noncausal models provides a brief history of microeconometrics, and a unique introduction to the issues of causality, structural relationships, and identification. The discussion in Chapter 3 about what can be learned from the types of data sets available to microeconometricians is especially informative and differs most substantially from what is available in other textbooks. 3.2. Part 2: Core Methods This section covers the core econometric theory that is the centerpiece of first-year texts such as Greene (2008), Hayashi (2000), or Ruud (2000). However, as discussed above, MMA has a broader coverage of topics which includes more advanced methods and a more complete discussion of D ow nl oa de d by [U niv ers ity of C ali fo rn ia, Sa n D ieg o] at 00 :43 25 M ay 20 13 Book Review 111 sticky implementation issues. The authors cover all the standard topics and provide introductions to all the standard formal results. Below, we mention some discussions that make this text special. It should be understood that all the standard topics and results are covered. In general, MMA is special because of its broad scope and intuitive discussions of more advanced topics. As discussed above, the authors discuss various problems encountered in practice with IV, such as the difficulty of finding good instruments and the possibility of weak instruments. The discussions of diagnostics for detecting weak instruments and the bias in the presence of weak instruments are concrete and helpful. The chapter on maximum likelihood and nonlinear least squares stands out by including more about estimating-equations estimators, the analogy principle, and estimators for models of the linear exponential family of densities. The treatment here provides more concrete guidance for applied work than the more theoretical treatments. Chapter 6, on generalized method of moments (GMM) estimators, covers optimal instruments and optimal moment conditions. The treatments of empirical likelihood and estimators based on moment conditions with nonadditive errors provide the student with an intuitive discussion of frontier methods. Chapter 7, on hypothesis testing, includes more detailed discussions of size and power than standard treatments. The authors perform Monte Carlo exercises to show in practice the distinction between asymptotic and actual size and power and show how to implement the Wald test using the bootstrap. Chapter 8, on specification tests and model selection, discusses the power of the Hausman test, pretest estimation, and data mining. The chapter closes with an insightful discussion about the role of specification testing in practice. In Chapter 9, the authors employ their signature level of scope and detail to semiparametric methods and they produce a unique discussion among the mainstream textbooks. While a detailed discussion of the theory of these topics is not presented, students are at least introduced to important terminology and key concepts from the theory. Chapter 10, on numerical optimization, covers some advanced methods, such as the Expectation Maximization (EM) algorithm and simulated annealing, and it provides some useful suggestions for checking code reliability. 3.3. Part 3: Simulation-Based Methods The application of computationally intensive techniques that exploit improvements in computer hardware and software is one of the most important advances in applied microeconomics in the past two decades. D ow nl oa de d by [U niv ers ity of C ali fo rn ia, Sa n D ieg o] at 00 :43 25 M ay 20 13 112 Book Review Microeconometrics has three chapters devoted to these methods. This concise summary of recent developments is very valuable for Ph.D. students and practitioners. Chapter 11 discusses bootstrap methods. This chapter has a self-contained description of the bootstrap and a sketch of the relevant econometric theory including the consistency of the bootstrap, Edgeworth expansions, and asymptotic refinements. Uses of the bootstrap in bias reduction, computing standard errors, hypothesis testing, confidence intervals, and other topics are covered. Simulation examples are provided. Extensions to the bootstrap, such as subsampling, the block bootstrap, the nested bootstrap, recentering, and the jackknife are discussed. Applications of the bootstrap to heteroskedastic errors, panel and clustered data, and overidentified GMM models, nonsmooth estimators, and time series are covered. The chapter closes with a discussion of some barriers that can preclude the use of the bootstrap in practice. Chapter 12 covers simulation-based estimation. After motivating these techniques, the authors discuss methods for computing integrals, including quadrature and Monte Carlo methods. The chapter then describes the mechanics of setting up maximum-simulated-likelihood (MSL) and method-of-simulated-moments (MSM) estimators. After discussing key theorems regarding consistency and asymptotic normality, the chapter provides a helpful comparison between MSL and MSM. The chapter also touches on indirect inference, importance sampling, variance reduction, and quasi-random numbers. The chapter closes with a detailed discussion of different methods for drawing random variables. Chapter 13 covers Bayesian methods. The chapter includes an overview of some key elements of Bayesian statistics. Bayesian methods have become increasingly common in both statistics and econometrics because of their computational advantages in certain problems. The chapter also covers Gibbs sampling, data augmentation, and the Metropolis–Hastings algorithm. It is obviously difficult to adequately summarize the recent advances in Bayesian methods that have occurred in the past two decades. However, the chapter at least introduces Ph.D. students to many important concepts and illustrates how to construct the simulators for a nontrivial simultaneous equations model. 3.4. Part 4: Models for Cross-Section Data As the introduction to the text emphasizes, the dependent variable in many applied studies is discrete, integer valued, or censored. The data may also come from a selected sample. This part covers methods used to analyze nonlinear, limited-dependent variable models. In addition to the standard topics, many semiparametric estimators are nicely treated. The authors provide a helpful discussion of the identification of selection D ow nl oa de d by [U niv ers ity of C ali fo rn ia, Sa n D ieg o] at 00 :43 25 M ay 20 13 Book Review 113 models using exclusion restrictions. The coverage of duration analysis is quite extensive compared to standard textbooks. 3.5. Part 5: Models for Panel Data This section covers the standard theory and recent research on estimators for the parameters of linear and nonlinear panel-data models. Much of this material is now standard and covered by Greene (2008) and Wooldridge (2002). When discussing dynamic models, the authors have a useful discussion on the distinction between true state dependence and unobserved heterogeneity. 3.6. Part 6: Further Topics Chapter 24 covers stratified and clustered samples. In practice, survey data sets are seldom based on random samples of the population. This chapter covers weighting schemes and the problem of endogenous stratification. In addition, techniques for clustering standard errors, such as cluster-robust standard errors, are presented. Different models for clustered data, diagnostics for clustering, and hierarchical linear models are also covered. Chapter 25 covers treatment evaluation. This topic is not covered in standard introductory econometrics textbooks and is an important addition given the wide use of these methods. This chapter discusses commonly used estimators such as matching, propensity-score methods, control-function estimators, regression-discontinuity-design, and difference-in-difference estimation. The chapter contains a fairly detailed discussion of the identification assumptions required for the alternative estimators. The different estimators of treatment effects are carefully compared in an example of the effect of training on earnings. Chapt