Estimating Autocorrelations in Fixed-Effects Models

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. A short time series of a large cross—section, however, 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 faciliate the identification and estimation of autocorrelation processes in both Nickell's method and an alternative method due to MaCurdy.