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.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 29 (2013)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/locate/ijforecast
Prediction; Panel data; Random effects; Serial correlation; AR(p);
Other versions of this item:
- Badi H. Baltagi & Long Liu, 2012. "Estimation and Prediction in the Random Effects Model with AR(p) Remainder Disturbances," Center for Policy Research Working Papers, Center for Policy Research, Maxwell School, Syracuse University 138, Center for Policy Research, Maxwell School, Syracuse University.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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