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

This paper presents specification tests that are applicable after estimating a dynamic model from panel data by the generalized method of moments (GMM), and studies the practical performance of these procedures using both generated and real data. Our GMM estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables. We propose a test of serial correlation based on the GMM residuals and compare this with Sargan tests of over-identifying restrictions and Hausman specification tests.

http://people.stern.nyu.edu/wgreene/Econometrics/Arellano-Bond.pdf

Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.

https://www.ucl.ac.uk/~uctp39a/Blundell-Bond-1998.pdf

Initial conditions and moment restrictions in dynamic panel data models

This paper develops a new approach to the problem of testing the existence of a level relationship between a dependent variable and a set of regressors, when it is not known with certainty whether the underlying regressors are trend- or first-difference stationary. The proposed tests are based on standard F- and t-statistics used to test the significance of the lagged levels of the variables in a univariate equilibrium correction mechanism. The asymptotic distributions of these statistics are non-standard under the null hypothesis that there exists no level relationship, irrespective of whether the regressors are I(0) or I(1). Two sets of asymptotic critical values are provided: one when all regressors are purely I(1) and the other if they are all purely I(0). These two sets of critical values provide a band covering all possible classifications of the regressors into purely I(0), purely I(1) or mutually cointegrated. Accordingly, various bounds testing procedures are proposed. It is shown that the proposed tests are consistent, and their asymptotic distribution under the null and suitably defined local alternatives are derived. The empirical relevance of the bounds procedures is demonstrated by a re-examination of the earnings equation included in the UK Treasury macroeconometric model. Copyright © 2001 John Wiley & Sons, Ltd.

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Bounds testing approaches to the analysis of level relationships

Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre”- and “post”-period and explicitly takes into account the effective sample size works well even for small numbers of states.

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How Much Should We Trust Differences-In-Differences Estimates?

Biases in Dynamic Models with Fixed Effects

Introduction to New Edition Preface to First Edition 1. Overview THE MICROFOUNDATIONS 2. Wage-Bargaining and Unions 3. Efficiency Wages 4. Wage Behaviour: the Evidence 5. Job Search: the Duration of Unemployment 6. Mismatch: the Structure of Unemployment 7. The Pricing and Employment Behaviour of Firms THE MACROECONOMIC OUTCOME 8. The Macroeconomics of Unemployment 9. Explaining Post-war Unemployment in OECD Countries POLICY IMPLICATIONS 10. Policies to Cut Unemployment Annexes Discussion Questions References

Unemployment: Macroeconomic Performance and the Labour Market

Are people right to think that competition improves corporate performance? My investigations indicate first that there are some theoretical reasons for believing this hypothesis to be correct, but they are not overwhelming. Furthermore, the existing empirical evidence on this question is weak. However, the results reported here, based on an analysis of around 670 U.K. companies, do provide some support for this view. Most important, I present evidence that competition, as measured by increased numbers of competitors or by lower levels of rents, is associated with a significantly higher rate of total factor productivity growth.

Competition and corporate performance

The received wisdom tells us that the rigidity and inflexibility of European job markets relative to that in the United States is the reason why Europe has high unemployment. This paper argues that this broad brush analysis is simply too vague to be useful. Indeed it is probably positively misleading. Many labor market institutions that conventionally come under the heading of rigidities have no observable impact on unemployment and may otherwise serve a useful purpose.

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Unemployment and Labor Market Rigidities: Europe versus North America

Barely a day goes by without some expert telling us how the continental European economies are about to disintegrate unless their labour markets become more flexible Basically, we are told, Europe has the wrong sort of labour market institutions for the modern global economy These outdated institutions both raise unemployment and lower growth rates The truth of propositions such as these depends on which labour market institutions really are bad for unemployment and growth, and which are not Our purpose in this paper is to set out what we know about this question Our conclusions indicate that the labour market institutions on which policy should be focused are unions and social security systems Encouraging product market competition is a key policy to eliminate the negative effects of unions For social security the key policies are benefit reform linked to active labour market policies to move people from welfare to work By comparison, time spent worrying about strict labour market regulations, employment protection and minimum wages is probably time largely wasted

Stephen Nickell

Papers

Biases in Dynamic Models with Fixed Effects

Unemployment: Macroeconomic Performance and the Labour Market

Competition and corporate performance

Unemployment and Labor Market Rigidities: Europe versus North America

Labor market institutions and economic performance