|This centre is a member of The LSE Research Laboratory [RLAB]: CASE | CVER | CEP | FMG | SERC | STICERD||Cookies?|
Peter M Robinson,
Paper No' EM/2007/515: | Full paper
Save Reference as: BibTeX File | EndNote Import File
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, is given to the source.
JEL Classification: C13; C14; C21
Is hard copy/paper copy available? YES - Paper Copy Still In Print.
This Paper is published under the following series: Econometrics
Share this page: Google Bookmarks | Facebook | Twitter
Abstract: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.
Copyright © STICERD & LSE 2005 - 2015 | LSE, Houghton Street, London WC2A 2AE | Tel: +44(0)20 7955 6699 | Email: email@example.com | Site updated 30 August 2015