Estimating Autocorrelations in Fixed-Effects Models
AbstractThis 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.
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Bibliographic InfoPaper provided by Princeton University, Department of Economics, Industrial Relations Section. in its series Working Papers with number 540.
Date of creation: Apr 1983
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Other versions of this item:
- Gary Solon, 1984. "Estimating Autocorrelations in Fixed-Effects Models," NBER Technical Working Papers 0032, National Bureau of Economic Research, Inc.
- M5 - Business Administration and Business Economics; Marketing; Accounting - - Personnel Economics
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