Efficient estimation for error component seemingly unrelated nonparametric regression models |
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Authors: | Bin?Zhou Qinfeng?Xu Email author" target="_blank">Jinhong?YouEmail author |
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Institution: | 1.Department of Statistics,East China Normal University,Shanghai,People’s Republic of China;2.Department of Statistics,Fudan University,Shanghai,People’s Republic of China;3.Department of Statistics,Shanghai University of Finance and Economics,Shanghai,People’s Republic of China |
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Abstract: | Multivariate panel data provides a unique opportunity in studying the joint evolution of multiple response variables over
time. In this paper, we propose an error component seemingly unrelated nonparametric regression model to fit the multivariate
panel data, which is more flexible than the traditional error component seemingly unrelated parametric regression. By applying
the undersmoothing technique and taking both of the correlations within and among responses into account, we propose an efficient
two-stage local polynomial estimation for the unknown functions. It is shown that the resulting estimators are asymptotically
normal, and have the same biases as the standard local polynomial estimators, which are only based on the individual response,
and smaller asymptotic variances. The performance of the proposed procedure is evaluated through a simulation study and a
real data set. |
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Keywords: | |
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