Does the choice of estimator matter when forecasting returns?

Joakim Westerlund, Paresh Kumar Narayan

Research output: Contribution to journalArticleResearchpeer-review

143 Citations (Scopus)

Abstract

While the literature concerned with the predictability of stock returns is huge, surprisingly little is known when it comes to role of the choice of estimator of the predictive regression. Ideally, the choice of estimator should be rooted in the salient features of the data. In case of predictive regressions of returns there are at least three such features; (i) returns are heteroskedastic, (ii) predictors are persistent, and (iii) regression errors are correlated with predictor innovations. In this paper we examine if the accounting of these features in the estimation process has any bearing on our ability to forecast future returns. The results suggest that it does.

Original languageEnglish
Pages (from-to)2632-2640
Number of pages9
JournalJournal of Banking and Finance
Volume36
Issue number9
DOIs
Publication statusPublished - Sep 2012
Externally publishedYes

Keywords

  • Heteroskedasticity
  • Predictive regression
  • Predictor endogeneity
  • Stock return predictability

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