Efficient estimation of the semiparametric spatial autoregressive model
| Autor: | Peter M. Robinson |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Pseudolikelihood
Statistics::Theory Economics and Econometrics Spatial autoregression Efficient estimation Adaptive estimation Simultaneity bias Estimation theory Applied Mathematics Nonparametric statistics jel:C13 jel:C21 jel:C14 Semiparametric model Efficient estimator Autoregressive model Statistics Econometrics Statistics::Methodology Semiparametric regression Parametric statistics Mathematics |
| Zdroj: | Journal of Econometrics. 157:6-17 |
| ISSN: | 0304-4076 |
| DOI: | 10.1016/j.jeconom.2009.10.031 |
| Popis: | Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic 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 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. One entails a stringent condition on the spatial weight matrix, and is suitable only when observations have substantially many "neighbours". The other adaptive estimate relaxes this requirement, at the expense of alternative conditions and possible computational expense. A Monte Carlo study of finite sample performance is included. |
| Databáze: | OpenAIRE |
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