Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/130103 
Year of Publication: 
2016
Series/Report no.: 
cemmap working paper No. CWP11/16
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
Cross-validation is the most common data-driven procedure for choosing smoothing parameters in nonparametric regression. For the case of kernel estimators with iid or strong mixing data, it is well-known that the bandwidth chosen by crossvalidation is optimal with respect to the average squared error and other performance measures. In this paper, we show that the cross-validated bandwidth continues to be optimal with respect to the average squared error even when the datagenerating process is a ß-recurrent Markov chain. This general class of processes covers stationary as well as nonstationary Markov chains. Hence, the proposed procedure adapts to the degree of recurrence, thereby freeing the researcher from the need to assume stationary (or nonstationary) before inference begins. We study finite sample performance in a Monte Carlo study. We conclude by demonstrating the practical usefulness of cross-validation in a highly-persistent environment, namely that of nonlinear predictive systems for market returns.
Subjects: 
Bandwidth Selection
Recurrence
Predictive Regressions
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
723.03 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.