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Testing for Parameter Instability in Predictive Regression Models

Georgiev, I and Harvey, DI and Leybourne, SJ and Taylor, AM (2018) Testing for Parameter Instability in Predictive Regression Models. Working Paper. Essex Finance Centre Working Papers. (Unpublished)

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Abstract

We consider tests for structural change, based on the SupF and Cramer-von-Mises type statistics of Andrews(1993) and Nyblom(1989), respectively, in the slope and/or intercept parameters of a predictive regression model where the predictors display strong persistence. The SupF type tests are motivated by alternatives where the parameters display a small number of breaks at deterministic points in the sample, while the Cramer-von-Mises alternative is one where the coefficients are random and slowly evolve through time. In order to allow for an unknown degree of persistence in the predictors, and for both conditional and unconditional heteroskedasticity in the data, we implement the tests using a� fixed regressor wild bootstrap procedure. The asymptotic validity of the bootstrap tests is established by showing that the asymptotic distributions of the bootstrap parameter constancy statistics, conditional on the data, coincide with those of the asymptotic null distributions of the corresponding statistics computed on the original data, conditional on the predictors. Monte Carlo simulations suggest that the bootstrap parameter stability tests work well in� finite samples, with the tests based on the Cramer-von-Mises type principle seemingly the most useful in practice. An empirical application to U.S. stock returns data demonstrates the practical usefulness of these methods.

Item Type: Monograph (Working Paper)
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 16 Jan 2018 10:27
Last Modified: 17 Oct 2019 18:16
URI: http://repository.essex.ac.uk/id/eprint/21162

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