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A time-space dynamic panel data model with spatial moving average errors
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DOI：10.1016/j.regsciurbeco.2018.04.013.png)
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Abstract
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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.
Keywords:
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
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