Confidence intervals for long-horizon predictive regressions via reverse regressions

Min Wei and Jonathan Wright

No 2009-27, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Long-horizon predictive regressions in finance pose formidable econometric problems when estimated using the sample sizes that are typically available. A remedy that has been proposed by Hodrick (1992) is to run a reverse regression in which short-horizon returns are projected onto a long-run mean of some predictor. By covariance stationarity, the slope coefficient is zero in the reverse regression if and only if it is zero in the original regression, but testing the hypothesis in the reverse regression avoids small sample problems. Unfortunately this only allows us to test the null of no predictability. In this paper we show how to use the reverse regression to test other hypotheses about the slope coefficient in a long-horizon predictive regression, and to form confidence intervals for this coefficient. We show that this approach to inference works well in small samples.

Keywords: Regression analysis; Stocks (search for similar items in EconPapers)
Date: 2009
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.federalreserve.gov/pubs/feds/2009/200927/200927abs.html (text/html)
http://www.federalreserve.gov/pubs/feds/2009/200927/200927pap.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2009-27

Access Statistics for this paper

More papers in Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.) Contact information at EDIRC.
Bibliographic data for series maintained by Ryan Wolfslayer ; Keisha Fournillier ().