A New Test In A Predictive Regression with Structural Breaks
Zongwu Cai and
Seong Yeon Chang
Additional contact information
Zongwu Cai: Department of Economics, The University of Kansas
Seong Yeon Chang: The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China
No 201811, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics
This paper considers predictive regressions where a structural break is allowed at some unknown date. We establish novel testing procedures for testing predictability via empirical likelihood methods based on some weighted score equations. Theoretical results are useful in practice because we adopt a unified framework under which it is unnecessary to distinguish whether the predictor variables are stationary or nonstationary. In particular, nonstationary predictor variables are allowed to be (nearly) integrated or exhibit a structural change at some unknown date. Simulations show that the empirical likelihood-based tests perform well in terms of size and power in finite samples. As an empirical analysis, we test asset returns predictability using various predictor variables.
Keywords: Autoregressive process; Empirical likelihood; Structural change; Unit root; Weighted estimation (search for similar items in EconPapers)
JEL-codes: C12 C14 C32 (search for similar items in EconPapers)
Date: 2018-12, Revised 2018-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:kan:wpaper:201811
Access Statistics for this paper
More papers in WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Professor Zongwu Cai ().