Estimation and Prediction in the Random Effects Model with AR(p) Remainder Disturbances
AbstractThis paper considers the problem of estimation and forecasting in a panel data model with random individual effects and AR(p) remainder disturbances. It utilizes a simple exact transformation for the AR(p) time series process derived by Baltagi and Li (1994) and obtains the generalized least squares estimator for this panel model as a least squares regression. This exact transformation is also used in conjunction with Goldberger’s (1962) result to derive an analytic expression for the best linear unbiased predictor. The performance of this predictor is investigated using Monte Carlo experiments and illustrated using an empirical example. Key Words: Prediction; Panel Data; Random Effects; Serial Correlation; AR(p) JEL Classification: C32
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Bibliographic InfoPaper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 138.
Length: 16 pages
Date of creation: Jul 2012
Date of revision:
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Other versions of this item:
- Baltagi, Badi H. & Liu, Long, 2013. "Estimation and prediction in the random effects model with AR(p) remainder disturbances," International Journal of Forecasting, Elsevier, vol. 29(1), pages 100-107.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-01-07 (All new papers)
- NEP-ECM-2013-01-07 (Econometrics)
- NEP-ETS-2013-01-07 (Econometric Time Series)
- NEP-FOR-2013-01-07 (Forecasting)
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