The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

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Econometric Analysis of Cross Section and Panel Data

In Acemoglu, Johnson, and Robinson, henceforth AJR, (2001), we advanced the hypothesis that the mortality rates faced by Europeans in different parts of the world after 1500 affected their willingness to establish settlements and choice of colonization strategy. Places that were relatively healthy (for Europeans) were—when they fell under European control—more likely to receive better economic and political institutions. In contrast, places where European settlers were less likely to go were more likely to have “extractive” institutions imposed. We also posited that this early pattern of institutions has persisted over time and influences the extent and nature of institutions in the modern world. On this basis, we proposed using estimates of potential European settler mortality as an instrument for institutional variation in former European colonies today. Data on settlers themselves are unfortunately patchy—particularly because not many went to places they believed, with good reason, to be most unhealthy. We therefore followed the lead of Curtin (1989 and 1998) who compiled data on the death rates faced by European soldiers in various overseas postings. 1 Curtin’s data were based on pathbreaking data collection and statistical work initiated by the British military in the mid-nineteenth century. These data became part of the foundation of both contemporary thinking about public health (for soldiers and for civilians) and the life insurance industry (as actuaries and executives considered the

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The Colonial Origins of Comparative Development: An Empirical Investigation

Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like 'what to do if there is choice-based sampling?' or 'when to measure effects?' can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes.

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Some practical guidance for the implementation of propensity score matching

This paper develops asymptotic distribution theory for instrumental variable regression when the partial correlation between the instruments and a single included endogenous variable is weak, here modeled as local to zero. Asymptotic representations are provided for various instrumental variable statistics, including the two-stage least squares (TSLS) and limited information maximum- likelihood (LIML) estimators and their t-statistics. The asymptotic distributions are found to provide good approximations to sampling distributions with just 20 observations per instrument. Even in large samples, TSLS can be badly biased, but LIML is, in many cases, approximately median unbiased. The theory suggests concrete quantitative guidelines for applied work. These guidelines help to interpret Angrist and Krueger's (1991) estimates of the returns to education: whereas TSLS estimates with many instruments approach the OLS estimate of 6%, the more reliable LIML and TSLS estimates with fewer instruments fall between 8% and 10%, with a typical confidence interval of (6%, 14%).

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Instrumental variables regression with weak instruments

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Quantile regression (QR) fits a linear model for conditional quantiles just as ordinary least squares (OLS) fits a linear model for conditional means. An attractive feature of OLS is that it gives the minimum mean-squared error linear approximation to the conditional expectation function even when the linear model is misspecified. Empirical research using quantile regression with discrete covariates suggests that QR may have a similar property, but the exact nature of the linear approximation has remained elusive. In this paper, we show that QR minimizes a weighted mean-squared error loss function for specification error. The weighting function is an average density of the dependent variable near the true conditional quantile. The weighted least squares interpretation of QR is used to derive an omitted variables bias formula and a partial quantile regression concept, similar to the relationship between partial regression and OLS. We also present asymptotic theory for the QR process under misspecification of the conditional quantile function. The approximation properties of QR are illustrated using wage data from the U.S. census. These results point to major changes in inequality from 1990 to 2000.

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Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure

The author uses micro data from the Labor Force Surveys conducted in the West Bank and the Gaza Strip during 1981-91 to show that during 1981-87 wage differences between schooling groups fell by well over one-half. This sharp reduction is associated with large increases in the size of the educated Palestinian labor force. Since the returns to schooling for Israeli Jews were stable, the decline in returns to schooling for Palestinians is consistent with the notion that the returns to schooling in the territories were determined largely by the forces of supply and demand in a segmented market for skilled labor. Copyright 1995 by American Economic Association.

The economic returns to schooling in the West Bank and Gaza strip

Grouped Data Estimation and Testing in Simple Labor Supply Models

Lottery estimates suggest Massachusetts’ urban charter schools boost achievement well beyond that of traditional urban public schools students, while nonurban charters reduce achievement from a higher baseline. The fact that urban charters are most effective for poor nonwhites and low-baseline achievers contributes to, but does not fully explain, these differences. We therefore link school-level charter impacts to school inputs and practices. The relative efficacy of urban lottery sample charters is accounted for by these schools’ embrace of the No Excuses approach to urban education. In our Massachusetts sample, Non-No-Excuses urban charters are no more effective than nonurban charters. (JEL H75, I21, I28) A growing body of evidence suggests that urban charter schools have the potential to generate impressive achievement gains, especially for minority students living in high-poverty areas. In a series of studies using admissions lotteries to identify causal effects, we looked at the impact of charter attendance in Boston and at a Knowledge is Power Program (KIPP) school in Lynn, Massachusetts (Abdulkadiro ˘ g

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Explaining Charter School Effectiveness

The charter school landscape includes a variety of organizational models and a few national franchises. The nation’s largest network of charter schools is the Knowledge is Power Program (KIPP), with 80 schools operating or slated to open soon. KIPP schools target low income and minority students and subscribe to an approach some have called No Excuses (Abigail Thernstrom and Stephen Thernstrom 2003). No Excuses schools feature a long school day and year, selective teacher hiring, strict behavior norms, and encourage a strong student work ethic. KIPP schools have often been central in the debate over whether schools alone can substantially reduce racial achievement gaps. Descriptive accounts of KIPP suggest positive achievement effects (see, e.g., Jay Mathews 2009), but critics argue that the apparent KIPP advantage reflects differences between students who attend traditional public schools and students that choose to attend KIPP schools (see, e.g., Martin Carnoy, Rebecca Jacobsen, Lawrence Mishel, and Richard Rothstein 2005). There are few well-controlled studies of KIPP schools that might help sort out these competing claims, and none that focus on KIPP. This paper reports on a quasi-experimental evaluation of the only KIPP school in New England, KIPP Academy Lynn. KIPP Lynn opened in the fall of 2004 and is the only charter school in Lynn, Mass., a low income city north of

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Joshua D. Angrist

Papers

Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure

The economic returns to schooling in the West Bank and Gaza strip

Grouped Data Estimation and Testing in Simple Labor Supply Models

Explaining Charter School Effectiveness

Inputs and Impacts in Charter Schools: KIPP Lynn