Robinson, Peter M. (2010) Efficient estimation of the semiparametric spatial autoregressive model. Journal of Econometrics, 157 (1). pp. 6-17. ISSN 0304-4076
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Official URL: | http://www.elsevier.com/locate/jeconom |
| Additional Information: | © 2009 Elsevier B.V. |
| Divisions: | Economics |
| Subjects: | H Social Sciences > HB Economic Theory H Social Sciences > HA Statistics |
| JEL classification: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models |
| Date Deposited: | 17 Aug 2010 09:59 |
| Last Modified: | 11 Dec 2024 23:41 |
| URI: | http://eprints.lse.ac.uk/id/eprint/28985 |
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