Estimating Autocorrelations in Fixed-Effects Models
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.
|Date of creation:||Feb 1984|
|Date of revision:|
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- MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January.
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NBER Working Papers
0150, National Bureau of Economic Research, Inc.
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- Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November.
- A. Bhargava & L. Franzini & W. Narendranathan, 1982. "Serial Correlation and the Fixed Effects Model," Review of Economic Studies, Oxford University Press, vol. 49(4), pages 533-549.
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- Stephen Nickell, 1980. "Correcting the Biases in Dynamic Models with Fixed Effects," Working Papers 513, Princeton University, Department of Economics, Industrial Relations Section..
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- Orley C. Ashenfelter & Gary Solon, 1982. "Longitudinal Labor Market Data: Sources, Uses, and Limitations," Working Papers 535, Princeton University, Department of Economics, Industrial Relations Section..
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