To Pool or Not to Pool: What Is a Good Strategy for Parameter Estimation and Forecasting in Panel Regressions?

35 Pages Posted: 24 Feb 2018 Last revised: 25 Nov 2018

See all articles by Wendun Wang

Wendun Wang

Erasmus University Rotterdam (EUR) - Department of Econometrics

Xinyu Zhang

Chinese Academy of Sciences (CAS) - Academy of Mathematics and Systems Sciences

Richard Paap

Erasmus University Rotterdam (EUR) - Department of Econometrics; Tinbergen Institute; Erasmus Research Institute of Management (ERIM)

Date Written: March 24, 2015

Abstract

This paper considers estimating the slope parameters and forecasting in potentially heterogeneous panel data regressions with a long time dimension. We propose a novel optimal pooling averaging estimator that makes an explicit trade-off between efficiency gains from pooling and bias due to heterogeneity. By theoretically and numerically comparing various estimators, we find that a uniformly best estimator does not exist and that our new estimator is superior in non-extreme cases and robust in extreme cases. Our results provide practical guidance for the best estimator and forecast depending on features of data and models. We apply our method to examine the determinants of sovereign credit default swap spreads and forecast future spreads.

Keywords: Credit default swap spreads; Heterogeneous panel; Model screening; Panel data forecasting; Pooling averaging

JEL Classification: C23, C52, G15

Suggested Citation

Wang, Wendun and Zhang, Xinyu and Paap, Richard, To Pool or Not to Pool: What Is a Good Strategy for Parameter Estimation and Forecasting in Panel Regressions? (March 24, 2015). Available at SSRN: https://ssrn.com/abstract=3123076 or http://dx.doi.org/10.2139/ssrn.3123076

Wendun Wang (Contact Author)

Erasmus University Rotterdam (EUR) - Department of Econometrics ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Xinyu Zhang

Chinese Academy of Sciences (CAS) - Academy of Mathematics and Systems Sciences ( email )

Zhong-Guan-Cun-Dong-Lu 55, Haidian District
Beijing, 100190, P.R., Beijing 100190
China

Richard Paap

Erasmus University Rotterdam (EUR) - Department of Econometrics ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Tinbergen Institute ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Erasmus Research Institute of Management (ERIM) ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

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