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作者

Badi H. Baltagi;Bernard Fingleton;Alain Pirotte

来源

REGIONAL SCIENCE AND URBAN ECONOMICS,Vol.76,Issue1,P.13-31

语言

英文

关键字

Panel data;Spatial lag;Error components;Time-space;Dynamic;OLS;Within;GM;Spatial autocorrelation;Direct and indirect effects;Moving average;Prediction;Simulations;Rook contiguity;Interregional trade;C23

作者单位

Department of Economics and Center for Policy Research, Syracuse University, United States;Department of Land Economy, University of Cambridge, United Kingdom;CRED, University Paris II Panthéon-Assas, France;Department of Economics and Center for Policy Research, Syracuse University, United States;Department of Land Economy, University of Cambridge, United Kingdom;CRED, University Paris II Panthéon-Assas, France

摘要

This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic panel data model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi et al. (2014) and Fingleton (2008a,b). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run effects and evaluate the predictive efficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001–2012, with the last two years reserved for prediction.