Predictive Systems: Living with Imperfect Predictors
University of Chicago - Booth School of Business; Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER)
Robert F. Stambaugh
University of Pennsylvania - The Wharton School; National Bureau of Economic Research (NBER)
CEPR Discussion Paper No. 6076
The standard regression approach to modeling return predictability seems too restrictive in one way but too lax in another. A predictive regression models expected returns as an exact linear function of a given set of predictors but does not exploit the likely economic property that innovations in expected returns are negatively correlated with unexpected returns. We develop an alternative framework - a predictive system - that accommodates imperfect predictors and beliefs about that negative correlation. In this framework, the predictive ability of imperfect predictors is supplemented by information in lagged returns as well as lags of the predictors. Compared to predictive regressions, predictive systems deliver different and substantially more precise estimates of expected returns as well as different assessments of a given predictor's usefulness.
Number of Pages in PDF File: 61
Keywords: Expected stock return, predictability, predictive regression, predictive system, state space model
JEL Classification: G1working papers series
Date posted: June 29, 2007
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