Estimation and Inference for Varying-coefficient Models with Nonstationary Regressors using Penalized Splines
Ying Fang () and
SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649
This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel-based methods, but enjoys computation advantages. Utilizing the mixed model representation of penalized splines, we develop a likelihood ratio test statistic for checking the stability of the regression coefficients. We derive both the exact and the asymptotic null distributions of this test statistic. We also demonstrate its optimality by examining its local power performance. These theoretical findings are well supported by simulation studies.
Keywords: Nonstationary Time Series; Varying-coeÂ±cient Model; Likelihood Ratio Test; Penalized Splines (search for similar items in EconPapers)
JEL-codes: C12 C14 C22 (search for similar items in EconPapers)
Pages: 47 pages
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: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Journal Article: ESTIMATION AND INFERENCE FOR VARYING-COEFFICIENT MODELS WITH NONSTATIONARY REGRESSORS USING PENALIZED SPLINES (2015)
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:hum:wpaper:sfb649dp2013-033
Access Statistics for this paper
More papers in SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649 Contact information at EDIRC.
Bibliographic data for series maintained by RDC-Team ().