Semiparametric Regression Estimation in Null Recurrent Nonlinear Time Series
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.
|Date of creation:||2009|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (618) 8303 5540
Web page: http://www.economics.adelaide.edu.au/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:adl:wpaper:2009-02. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dmitriy Kvasov)
If references are entirely missing, you can add them using this form.