出版社：Suntory Toyota International Centre for Economics and Related Disciplines
摘要：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.
关键词：Kernel Estimation; Forecasting; Panel Data; Unit Roots
