Premium
Estimating and Forecasting with a Dynamic Spatial Panel Data Model *
Author(s) -
Baltagi Badi H.,
Fingleton Bernard,
Pirotte Alain
Publication year - 2014
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12011
Subject(s) - autoregressive model , estimator , panel data , generalized method of moments , econometrics , spatial dependence , spatial econometrics , lag , monte carlo method , spatial analysis , statistics , computer science , mathematics , computer network
This study 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 generalized method of moments (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.