Journal ArticleDOI
Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.
Manuel Arellano,Stephen Bond +1 more
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In this article, the generalized method of moments (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.Abstract:
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.read more
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Book
Econometric Analysis of Cross Section and Panel Data
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Report SeriesDOI
Initial conditions and moment restrictions in dynamic panel data models
Richard Blundell,Stephen Bond +1 more
TL;DR: In this paper, two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator are presented. But both estimators require restrictions on the initial conditions process.
Journal ArticleDOI
Another look at the instrumental variable estimation of error-components models
Manuel Arellano,Olympia Bover +1 more
TL;DR: In this paper, a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables is presented. But the authors do not consider models with predetermined variables that have constant correlation with the effects.
Journal ArticleDOI
How to do Xtabond2: An Introduction to Difference and System GMM in Stata
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.
Journal ArticleDOI
How to do xtabond2: An introduction to difference and system GMM in Stata
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.
References
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Journal ArticleDOI
Specification Tests in Econometrics
TL;DR: In this article, the null hypothesis of no misspecification was used to show that an asymptotically efficient estimator must have zero covariance with its difference from a consistent but asymptonically inefficient estimator, and specification tests for a number of model specifications in econometrics.
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Large sample properties of generalized method of moments estimators
Journal ArticleDOI
Estimating vector autoregressions with panel data
TL;DR: In this article, the authors consider estimation and testing of vector autoregressio n coefficients in panel data, and apply the techniques to analyze the dynamic relationships between wages an d hours worked in two samples of American males.
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The estimation of economic relationships using instrumental variables
TL;DR: In this article, the asymptotic error variance matrix for the coefficients of one of the relationships is obtained in the case in which these relationships are estimated using instrumental variables, and the problem of choice that arises when there are more instrumental variables available than the minimum number required to enable the method to be used is discussed.
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Panel data and unobservable individual effects
TL;DR: In this article, the authors derived a test for the presence of this effect and for the over-identifying restriction they use; necessary and sufficient conditions for identification of all the parameters in the model; and the asymptotically efficient instrumental variables estimator and conditions under which it differs from the within-groups estimator.