The University of Southampton
University of Southampton Institutional Repository

Inferring the predictability induced by a persistent regressor in a predictive threshold model

Inferring the predictability induced by a persistent regressor in a predictive threshold model
Inferring the predictability induced by a persistent regressor in a predictive threshold model
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 à 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 characterised by threshold effects or not (e.g. shifting versus non-shifting 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.
predictive regressions, threshold effects, predictability of stock returns
0735-0015
202-217
Gonzalo, Jesùs
0ec956ab-f13e-4466-bff4-4b2923290ae5
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Gonzalo, Jesùs
0ec956ab-f13e-4466-bff4-4b2923290ae5
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51

Gonzalo, Jesùs and Pitarakis, Jean-Yves (2017) Inferring the predictability induced by a persistent regressor in a predictive threshold model. Journal of Business and Economic Statistics, 35 (2), 202-217. (doi:10.1080/07350015.2016.1164054).

Record type: Article

Abstract

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 à 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 characterised by threshold effects or not (e.g. shifting versus non-shifting 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.

Text
Inferring the Predictability Induced by a Persistent Regressor in a Predictive Threshold Model.pdf - Accepted Manuscript
Download (496kB)

More information

Accepted/In Press date: 28 February 2016
e-pub ahead of print date: 13 March 2017
Published date: 13 March 2017
Keywords: predictive regressions, threshold effects, predictability of stock returns
Organisations: Economics

Identifiers

Local EPrints ID: 390672
URI: http://eprints.soton.ac.uk/id/eprint/390672
ISSN: 0735-0015
PURE UUID: c9678560-de11-4ac6-898a-891bb11ab96e
ORCID for Jean-Yves Pitarakis: ORCID iD orcid.org/0000-0002-6305-7421

Catalogue record

Date deposited: 04 Apr 2016 16:10
Last modified: 15 Mar 2024 05:27

Export record

Altmetrics

Contributors

Author: Jesùs Gonzalo

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×