Efficient Estimation of the SemiparametricSpatial Autoregressive Model

Peter M Robinson

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: Efficient semiparametric and parametric estimates are developed for aspatial autoregressive model, containing nonstochastic explanatoryvariables and innovations suspected to be non-normal. The main stress ison the case of distribution of unknown, nonparametric, form, where seriesnonparametric estimates of the score function are employed in adaptiveestimates of parameters of interest. These estimates are as efficient asones based on a correct form, in particular they are more efficient thanpseudo-Gaussian maximum likelihood estimates at non-Gaussiandistributions. Two different adaptive estimates are considered. One entails astringent condition on the spatial weight matrix, and is suitable only whenobservations have substantially many "neighbours". The other adaptiveestimate relaxes this requirement, at the expense of alternative conditionsand possible computational expense. A Monte Carlo study of finite sampleperformance is included.

Keywords: Spatial autoregression; Efficient estimation; Adaptive estimation; Simultaneity bias.© The author. All rights reserved. Short sections of text; not to exceed two paragraphs; may be quoted without explicit permission provided that full credit; including © notice; isgiven to the source. (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 (search for similar items in EconPapers)
Date: 2007-02
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:515

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