|This centre is a member of The LSE Research Laboratory [RLAB]: CASE | CVER | CEP | SERC | STICERD||Cookies?|
Paper No' SERCDP0095: | Full paper
Save Reference as: BibTeX File | EndNote Import File
Keywords: Panel data; spatial lag; error components; linear predictor; GMM; spatial autocorrelation
JEL Classification: C33
Is hard copy/paper copy available? YES - Paper Copy Still In Print.
This Paper is published under the following series: SERC Discussion Papers
Share this page: Google Bookmarks | Facebook | Twitter
Abstract:This paper focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006), a dynamic spatial GMM estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the Spatial AutoRegressive (SAR) error model. The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non-spatial estimators and illustrate our approach with an application to new economic geography.
Copyright © RLAB & LSE 2003 - 2019 | LSE, Houghton Street, London WC2A 2AE | Contact: RLAB | Site updated 20 October 2019