The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators

Javier Álvarez and Manuel Arellano

Working Papers from CEMFI

Abstract: In this paper we derive the asymptotic properties of within groups (WG), GMM and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N->0 the fixed T results for GMM and LIML remain valid, but WG although consistent has an asymptotic bias in its asymptotic distribution. When T/N tends to a positive constant, the WG, GMM and LIML estimators exhibit negative asymptotic biases of order T,N and (2N-T), respectively. In addition, the crude GMM estimator that neglects the autocorrelation in first differenced errors is inconsistent as T/N->c>0, despite being consistent for fixed T. Finally, we discuss the properties of a random effects MLE with unrestricted initial conditions when both T and N tend to infinity.

Date: 1998
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Citations: View citations in EconPapers (15)

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Related works:
Journal Article: The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators (2003)
Working Paper: The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators (1998)
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Persistent link: https://EconPapers.repec.org/RePEc:cmf:wpaper:wp1998_9808

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