Kernel estimation of functional coefficients in nonparametric ARX time series models
This paper suggests a general functional-coefficient regression model in a form of ARX time series model. Contrast to the common threshold variable in the previous works, our model allows each coefficient to possess a different threshold variable and can cover a wide range of nonlinear dynamic processes. The estimation procedure consists of two steps; local linear smoothing and marginal integration. The asymptotic normality of the proposed estimator is derived with the explicit form of bias and variance.
|Date of creation:||2001|
|Date of revision:|
|Contact details of provider:|| Postal: Spandauer Str. 1,10178 Berlin|
Web page: http://www.wiwi.hu-berlin.de/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:zbw:sfb373:2001101. 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: (ZBW - German National Library of Economics)
If references are entirely missing, you can add them using this form.