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 principle seemingly the most useful in practice. An empirical application to U.S. stock returns data demonstrates the practical usefulness of these methods.
Highlights
Predictive regression is a widely used tool in applied finance and economics
As the results in the previous section show, implementing tests based on the LM and SupF statistics will require us to address the fact that their limiting null distributions depend on any unconditional heteroskedasticity present in εxt and εyt, and on the persistence parameter cx
B 1x the fixed regressor wild bootstrap procedure outlined in Algorithm 1, whereby the original statistic is compared to its empirical bootstrap critical value, cvξ,B
Summary
Predictive regression (hereafter PR) is a widely used tool in applied finance and economics. Georgiev et al / Journal of Econometrics 204 (2018) 101–118 changes in a model’s parameters to investigate the structural stability of PRs for stock returns related to structural breaks in the coefficients of state variables (including the lagged dividend yield, short interest rate, term spread and default premium) for a data-set of monthly stock returns for ten OECD countries They find evidence of instability for the vast majority of these countries, arguing that the ‘‘Empirical evidence of predictability is not uniform over time and is concentrated in certain periods’’. Doing so introduces the considerable complication relative to the case of a pure unit root regressor that the limiting null distributions of the parameter constancy statistics depend on the local-to-unity (persistence) parameter of the putative predictor In principle, this makes it very difficult to control the size of the tests given that this parameter is unknown in practice and cannot be consistently estimated.. Additional material relating to the limiting distributions of the statistics given in Section 3 is provided in an accompanying on-line supplementary appendix
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
