2009
DOI: 10.1111/j.1368-423x.2008.00260.x
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On the impact of error cross-sectional dependence in short dynamic panel estimation

Abstract: This paper explores the impact of error cross-sectional dependence (modelled as a factor structure) on a number of widely used IV and generalized method of moments (GMM) estimators in the context of a linear dynamic panel data model. It is shown that, under such circumstances, the standard moment conditions used by these estimators are invalid -- a result that holds for any lag length of the instruments used. Transforming the data in terms of deviations from time-specific averages helps to reduce the asymptoti… Show more

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Cited by 165 publications
(82 citation statements)
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“…Second, the problem of cross‐sectional error dependence can lead to serious problems in the estimation of short dynamic panels. Sarafidis and Robertson (2009) demonstrate that under cross‐sectional error dependence, the GMM estimator is inconsistent as N →∞ for fixed T , which holds for any lag length of the instruments used.…”
Section: Methodsmentioning
confidence: 83%
“…Second, the problem of cross‐sectional error dependence can lead to serious problems in the estimation of short dynamic panels. Sarafidis and Robertson (2009) demonstrate that under cross‐sectional error dependence, the GMM estimator is inconsistent as N →∞ for fixed T , which holds for any lag length of the instruments used.…”
Section: Methodsmentioning
confidence: 83%
“…In the same line of argument, GMM estimates are also inconsistent because the moment conditions used by GMM are violated as N ? ∞ for fixed T (Sarafidis & Robertson, 2009). Westerlund and Edgerton (2008, p. 666) note that:…”
Section: Estimation Methodsmentioning
confidence: 97%
“…Moscone and Tosetti () point out that when the data are cross‐sectionally dependent, the conventional estimates are inefficient and estimated standard errors are biased. In the same line of argument, GMM estimates are also inconsistent because the moment conditions used by GMM are violated as N → ∞ for fixed T (Sarafidis & Robertson, ). Westerlund and Edgerton (, p. 666) note that:
…important problem is that the first generation of tests has been unable to handle cross‐sectional dependence.
…”
Section: Estimation Methodsmentioning
confidence: 99%
“…The IV approach is preferable when errors show a cross-sectional dependence, as in our case (Sarafidis and Robertson, 2009) and where institutions are characterized by high persistence across time, as in Italy. In the presence of interaction terms in the model, moreover, the two-stage IV strategy can be the most parsimonious way of addressing endogeneity (Wooldridge, 2010).…”
Section: Addressing the Endogeneity Of The Institutional Variablementioning
confidence: 99%