WALS Prediction

J.R. Magnus, W. Wang, Xinyu Zhang

Research output: Working paperDiscussion paperOther research output

252 Downloads (Pure)

Abstract

Abstract: Prediction under model uncertainty is an important and difficult issue. Traditional prediction methods (such as pretesting) are based on model selection followed by prediction in the selected model, but the reported prediction and the reported prediction variance ignore the uncertainty from the selection procedure. This paper proposes a weighted average least squares (WALS) prediction procedure that is not conditional on the selected model. Taking both model and error uncertainty into account, we also propose an appropriate estimate of the variance of the WALS predictor. Correlations among the random errors are explicitly allowed. Compared to other prediction averaging methods, the WALS predictor has important advantages both theoretically and computationally. Simulation studies show that the WALS predictor generally produces lower mean squared prediction errors than its competitors, and that the proposed estimator for the prediction variance performs particularly well when model uncertainty increases.
Original languageEnglish
Place of PublicationTilburg
PublisherEconometrics
Number of pages38
Volume2012-043
Publication statusPublished - 2012

Publication series

NameCentER Discussion Paper
Volume2012-043

Keywords

  • Model averaging
  • Model uncertainty
  • Bayesian analysis
  • Prediction

Fingerprint Dive into the research topics of 'WALS Prediction'. Together they form a unique fingerprint.

  • Cite this

    Magnus, J. R., Wang, W., & Zhang, X. (2012). WALS Prediction. (CentER Discussion Paper; Vol. 2012-043). Econometrics.