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Efficient estimation of the semiparametric spatial autoregressive model

By Peter M. Robinson

Abstract

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing non-stochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series nonparametric estimates of the score function are employed in adaptive estimates of parameters of interest. These estimates are as efficient as the ones based on a correct form, in particular they are more efficient than pseudo-Gaussian maximum likelihood estimates at non-Gaussian distributions. Two different adaptive estimates are considered, relying on somewhat different regularity conditions. A Monte Carlo study of finite sample performance is included. (C) 2009 Elsevier B.V. All rights reserved

Topics: HB Economic Theory, HA Statistics
Publisher: Elsevier
Year: 2010
DOI identifier: 10.1016/j.jeconom.2009.10.031
OAI identifier: oai:eprints.lse.ac.uk:28985
Provided by: LSE Research Online
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