Nonparametric Trending Regression with Cross-Sectional Dependence
نویسنده
چکیده
Panel data, whose series length T is large but whose cross-section size N need not be, are assumed to have a common time trend. The time trend is of unknown form, the model includes additive, unknown, individual-speci c components, and we allow for spatial or other cross-sectional dependence and/or heteroscedasticity. A simple smoothed nonparametric trend estimate is shown to be dominated by an estimate which exploits the availability of cross-sectional data. Asymptotically optimal choices of bandwidth are justi ed for both estimates. Feasible optimal bandwidths, and feasible optimal trend estimates, are asymptotically justi ed, the nite sample performance of the latter being examined in a Monte Carlo study. A number of potential extensions are discussed. JEL Classi cations: C13; C14; C23
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