Semiparametric Regression Estimation in Null Recurrent Nonlinear Time Series

Jia Chen, Jiti Gao and Degui Li

No 2009-02, School of Economics and Public Policy Working Papers from University of Adelaide, School of Economics and Public Policy

Abstract: Estimation theory in a nonstationary environment has been very popular in recent years. Existing studies focus on nonstationarity in parametric linear, parametric nonlinear and nonparametric nonlinear models. In this paper, we consider a partially linear model and propose to estimate both alpha and g semiparametrically. We then show that the proposed estimator of alpha is still asymptotically normal with the same rate as for the case of stationary time series. We also establish the asymptotic normality for the nonparametric estimator of the function g and the uniform consistency of the nonparametric estimator. The simulated example is given to show that our theory and method work well in practice.

Keywords: asymptotic normality; beta-null recurrent Markov chain; consistency; kernel estimator; partially linear model (search for similar items in EconPapers)
Pages: 54 pages
Date: 2009
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:adl:wpaper:2009-02

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