Estimating Autocorrelations in Fixed-Effects Models

49 Pages Posted: 22 Apr 2004 Last revised: 5 Aug 2010

See all articles by Gary Solon

Gary Solon

University of Arizona; National Bureau of Economic Research (NBER)

Date Written: February 1984

Abstract

This paper discusses the estimation of serial correlation in fixed effects models for longitudinal data. Like time series data, longitudinal data often contain serially correlated error terms, but the autocorrelation estimators commonly used for time series, which are consistent as the length of the time series goes to infinity, are not consistent for a short time series as the size of the cross-section goes to infinity. This form of inconsistency is of particular concern because a short time series of a large cross-section is the typical case in longitudinal data. This paper extends Nickell's method of correcting for the inconsistency of autocorrelation estimators by generalizing to higher than first-order autocorrelations and to error processes other than first-order autoregressions. The paper also presents statistical tables that facilitate the identification and estimation of autocorrelation processes in both the generalized Nickell method and an alternative method due to MaCurdy. Finally, the paper uses Monte Carlo methods to explore the finite-sample properties of both methods.

Suggested Citation

Solon, Gary, Estimating Autocorrelations in Fixed-Effects Models (February 1984). NBER Working Paper No. t0032. Available at SSRN: https://ssrn.com/abstract=324000

Gary Solon (Contact Author)

University of Arizona ( email )

Department of Economics
Eller College of Management
Tucson, AZ 85719
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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