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Semiparametric Regression Estimation in Null Recurrent Nonlinear Time Series

Listed author(s):
  • Jia Chen

    (School of Economics, University of Adelaide)

  • Jiti Gao


    (School of Economics, The University of Adelaide)

  • Degui Li

    (School of Economics, University of Adelaide)

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.

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File Function: First version, 2009
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Paper provided by University of Adelaide, School of Economics in its series School of Economics Working Papers with number 2009-02.

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Length: 54 pages
Date of creation: 2009
Handle: RePEc:adl:wpaper:2009-02
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