City Research Online

Abstract

In this paper we investigate a class of semiparametric models for panel datasets where the cross-section and time dimensions are large. Our model contains a latent time series that is to be estimated and perhaps forecasted along with a nonparametric covariate effect. Our model is motivated by the need to be flexible with regard to functional form of covariate effects but also the need to be practical with regard to forecasting of time series effects. We propose estimation procedures based on local linear kernel smoothing; our estimators are all explicitly given. We establish the pointwise consistency and asymptotic normality of our estimators. We also show that the effects of estimating the latent time series can be ignored in certain cases.

Publication Type:	Article
Additional Information:	This is the accepted version of the following article: Linton, O, Nielsen, JP & Nielsen, SF (2009). Non-parametric regression with a latent time series. ECONOMETRICS JOURNAL, 12(2), pp. 187-207, which has been published in final form at DOI: 10.1111/j.1368-423X.2009.00278.x
Publisher Keywords:	Kernel Estimation; Forecasting; Panel Data; Unit Roots
Subjects:	H Social Sciences > HB Economic Theory
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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