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

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  • Haiqiang Chen
  • Ying Fang
  • Yingxing Li

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.

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File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2013-033.pdf
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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2013-033.

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Length: 47 pages
Date of creation: Jul 2013
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2013-033

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Related research

Keywords: Nonstationary Time Series; Varying-coe±cient Model; Likelihood Ratio Test; Penalized Splines;

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References

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Cited by:
  1. Poeschel, Friedrich, 2012. "Assortative matching through signals," IAB Discussion Paper 201215, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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