EconPapers    
Economics at your fingertips  
 

Data-Driven Bandwidth Selection for Nonstationary Semiparametric Models

Yiguo Sun and Qi Li

Journal of Business & Economic Statistics, 2011, vol. 29, issue 4, 541-551

Abstract: This article extends the asymptotic results of the traditional least squares cross-validatory (CV) bandwidth selection method to semiparametric regression models with nonstationary data. Two main findings are that (a) the CV-selected bandwidth is stochastic even asymptotically and (b) the selected bandwidth based on the local constant method converges to 0 at a different speed than that based on the local linear method. Both findings are in sharp contrast to existing results when working with weakly dependent or independent data. Monte Carlo simulations confirm our theoretical results and show that the automatic data-driven method works well.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1198/jbes.2011.09159 (text/html)
Access to full text is restricted to subscribers.

Related works:
Journal Article: Data-Driven Bandwidth Selection for Nonstationary Semiparametric Models (2011) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:29:y:2011:i:4:p:541-551

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1198/jbes.2011.09159

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-31
Handle: RePEc:taf:jnlbes:v:29:y:2011:i:4:p:541-551