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Dynamic panel data models: a guide to microdata methods and practice

03 Apr 2002-Research Papers in Economics (Centre for Microdata Methods and Practice, Institute for Fiscal Studies)-
TL;DR: In this article, the focus is on panels where a large number of individuals or firms are observed for a small number of time periods, typical of applications with microeconomic data, and the emphasis is on single equation models with autoregressive dynamics and explanatory variables.
Abstract: This paper reviews econometric methods for dynamic panel data models, and presents examples that illustrate the use of these procedures. The focus is on panels where a large number of individuals or firms are observed for a small number of time periods, typical of applications with microeconomic data. The emphasis is on single equation models with autoregressive dynamics and explanatory variables that are not strictly exogenous, and hence on the Generalised Method of Moments estimators that are widely used in this context. Two examples using firm-level panels are discussed in detail: a simple autoregressive model for investment rates; and a basic production function.
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Journal ArticleDOI
TL;DR: This pedagogic paper first introduces linear GMM, and shows how limited time span and the potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way.
Abstract: This working paper by CGD research fellow David Roodman provides an introduction to a particular class of econometric techniques, dynamic panel estimators. The techniques and their implementation in Stata, a statistical software package widely used in the research community, are an important input to the careful applied research CGD advocates. The techniques discussed are specifically designed to extract causal lessons from data on a large number of individuals (whether countries, firms or people) each of which is observed only a few times, such as annually over five or ten years. These techniques were developed in the 1990s by authors such as Manuel Arellano, Richard Blundell and Olympia Bover, and have been widely applied to estimate everything from the impact of foreign aid to the importance of financial sector development to the effects of AIDS deaths on households. The present paper contributes to this literature pedagogically, by providing an original synthesis and exposition of the literature on these dynamic panel estimators, and practically, by presenting the first implementation of some of these techniques in Stata. Stata is designed to encourage users to develop new commands for it, which other users can then use or even modify. In this paper Roodman introduces abar and xtabond2, which is now one of the most frequently downloaded user-written Stata commands in the world. Stata's partially open-source architecture has encouraged the growth of a vibrant world-wide community of researchers, which benefits not only from improvements made to Stata by the parent corporation, but also from the voluntary contributions of other users. Stata is arguably one of the best examples of a combination of private for-profit incentives and voluntary open-source incentives in the joint creation of a global public good.

5,458 citations

Journal ArticleDOI
TL;DR: This paper introduced linear generalized method of moments (GMM) estimators for situations with small T, large N panels, with independent variables that are not strictly exogenous, meaning correlated with past and possibly current realizations of the error; with fixed effects; and with heteroskedasticity and autocorrelation within individuals.
Abstract: The Arellano-Bond (1991) and Arellano-Bover (1995)/Blundell-Bond (1998) linear generalized method of moments (GMM) estimators are increasingly popular. Both are general estimators designed for situations with “small T, large N” panels, meaning few time periods and many individuals; with independent variables that are not strictly exogenous, meaning correlated with past and possibly current realizations of the error; with fixed effects; and with heteroskedasticity and autocorrelation within individuals. This pedagogic paper first introduces linear GMM. Then it shows how limited time span and the potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way. Next it shows how to apply these estimators with xtabond2. It also explains how to perform the Arellano-Bond test for autocorrelation in a panel after other Stata commands, using abar. The Center for Global Development is an independent think tank that works to reduce global poverty and inequality through rigorous research and active engagement with the policy community. Use and dissemination of this Working Paper is encouraged, however reproduced copies may not be used for commercial purposes. Further usage is permitted under the terms of the Creative Commons License. The views expressed in this paper are those of the author and should not be attributed to the directors or funders of the Center for Global Development.

5,416 citations

Journal ArticleDOI
TL;DR: This article reviewed the evidence on the effects of instrument proliferation, and described and simulated simple ways to control it, and illustrated the dangers by replicating Forbes [American Economic Review (2000) Vol. 90, pp. 869-887] on income inequality and Levine et al. [Journal of Monetary Economics] (2000] Vol. 46, pp 31-77] on financial sector development.
Abstract: The difference and system generalized method of moments (GMM) estimators are growing in popularity. As implemented in popular software, the estimators easily generate instruments that are numerous and, in system GMM, potentially suspect. A large instrument collection overfits endogenous variables even as it weakens the Hansen test of the instruments’ joint validity. This paper reviews the evidence on the effects of instrument proliferation, and describes and simulates simple ways to control it. It illustrates the dangers by replicating Forbes [American Economic Review (2000) Vol. 90, pp. 869–887] on income inequality and Levine et al. [Journal of Monetary Economics] (2000) Vol. 46, pp. 31–77] on financial sector development. Results in both papers appear driven by previously undetected endogeneity.

3,429 citations

Journal ArticleDOI
TL;DR: In this paper, the authors review the evidence on the effects of instrument proliferation, and describes and simulates simple ways to control it, and illustrate the dangers by replicating two early applications to economic growth: Forbes (2000) on income inequality and Levine, Loayza, and Beck (2000).
Abstract: The Difference and System generalized method of moments (GMM) estimators are growing in popularity, thanks in part to specialized software. But as implemented in these packages, the estimators easily generate results by default that are at once invalid yet appear valid in specification tests. The culprit is their tendency to generate instruments that are a) numerous and, in System GMM, b) suspect. A large collection of instruments, even if individually valid, can be collectively invalid in finite samples because they overfit endogenous variables. They also weaken the Hansen test of overidentifying restrictions, which is commonly relied upon to check instrument validity. This paper reviews the evidence on the effects of instrument proliferation, and describes and simulates simple ways to control it. It illustrates the dangers by replicating two early applications to economic growth: Forbes (2000) on income inequality and Levine, Loayza, and Beck (2000) on financial sector development. Results in both papers appear driven by previously undetected endogeneity.

3,350 citations

Journal ArticleDOI
TL;DR: In this article, the authors use a well-developed dynamic panel generalized method of moments estimator to alleviate endogeneity concerns in two aspects of corporate governance research: the effect of board structure on firm performance and the determinants of board structures.

1,580 citations