Title(题名):  Inferring the Predictabihty Induced by a Persistent Regressor in a Predictive Threshold Model
Authors(作者):  Jesùs Gonzalo and Jean-Yves Pitarakis
Source title(刊名):  Journal of Business &Economic Statistics:A Publication of the American Statistical Association
Volume, Issue, Issue date
 April 2017,Vol35,Issue2
Pages(页码):  p202-217
ISSN:  0735-0015
Abstract(摘要):  We develop tests for detecting possibly episodic predictability induced by a persistent predictor. Our framework is that of a predictive regression model with threshold effects and our goal is to develop operational and easily implementable inferences when one does not wish to impose a priori restrictions on the parameters of the model other than the slopes corresponding to the persistent predictor. Differently put our tests for the null hypothesis of no predictability against threshold predictability remain valid without the need to know whether the remaining parameters of the model are characterized by threshold effects or not (e.g., shifting versus nonshifting intercepts). One interesting feature of our setting is that our test statistics remain unaffected by whether some nuisance parameters are identified or not. We subsequently apply our methodology to the predictability of aggregate stock returns with valuation ratios and document a robust countercyclicality in the ability of some valuation ratios to predict returns in addition to highlighting a strong sensitivity of predictability based results to the time period under consideration.
Key words(关键词):  Predictability of stock returns; Predictive regressions; Threshold effects.
Where(馆藏地):  304外文报刊阅览室
Available online
 2017/12/28 14:20:34
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