Efficient Regression in Time Series Partial Linear Models

Peter Phillips, Binbin Guo and Zhijie Xiao
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Binbin Guo: UCLA, Santa Cruz

No 1363, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper studies efficient estimation of partial linear regression in time series models. In particular, it combines two topics that have attracted a good deal of attention in econometrics, viz. spectral regression and partial linear regression, and proposes an efficient frequency domain estimator for partial linear models with serially correlated residuals. A nonparametric treatment of regression errors is permitted so that it is not necessary to be explicit about the dynamic specification of the errors other than to assume stationarity. A new concept of weak dependence is introduced based on regularity conditions on the joint density. Under these and some other regularity conditions, it is shown that the spectral estimator is root-n-consistent, asymptotically normal, and asymptotically efficient.

Keywords: Efficient estimation; Partial linear regression; Spectral regression; Kernel estimation; Nonparametric; Semiparametric; Weak dependence (search for similar items in EconPapers)
JEL-codes: C13 C14 C22 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2002-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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