A Combined Random Effect and Fixed Effect Forecast for Panel Data Models

Tae Hwy Lee, Bai Huang () and Aman Ullah
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Bai Huang: CUFE

No 201906, Working Papers from University of California at Riverside, Department of Economics

Abstract: When some of the regressors in a panel data model are correlated with the random individual effects, the random effect (RE) estimator becomes inconsistent while the fixed effect (FE) estimator is consistent. Depending on the various degree of such correlation, we can combine the RE estimator and FE estimator to form a combined estimator which can be better than each of the FE and RE estimators. In this paper, we are interested in whether the combined estimator may be used to form a combined forecast to improve upon the RE forecast (forecast made using the RE estimator) and the FE forecast (forecast using the FE estimator) in out-of-sample forecasting. Our simulation experiment shows that the combined forecast does dominate the FE forecast for all degrees of endogeneity in terms of mean squared forecast errors (MSFE), demonstrating that the theoretical results of the risk dominance for the in-sample estimation carry over to the out-of-sample forecasting. It also shows that the combined forecast can reduce MSFE relative to the RE forecast for moderate to large degrees of endogeneity and for large degrees of heterogeneity in individual effects.

Keywords: Endogeneity; Panel Data; Fixed Effect; Random Effect; Hausman test; Combined Estimator; Combined Forecast. (search for similar items in EconPapers)
JEL-codes: C13 C33 C52 (search for similar items in EconPapers)
Pages: 30 Pages
Date: 2018-12
New Economics Papers: this item is included in nep-dcm, nep-ecm, nep-for and nep-ore
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https://economics.ucr.edu/repec/ucr/wpaper/201906.pdf First version, 2018 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201906

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