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Estimation and Inference for Varying-coefficient Models with Nonstationary Regressors using Penalized Splines

Haiqiang Chen, Ying Fang () and Yingxing Li

SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

Abstract: 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-coe±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
Date: 2013-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Related works:
Journal Article: ESTIMATION AND INFERENCE FOR VARYING-COEFFICIENT MODELS WITH NONSTATIONARY REGRESSORS USING PENALIZED SPLINES (2015) Downloads
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