A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors

Badi Baltagi, Bernard Fingleton () and Alain Pirotte

MPRA Paper from University Library of Munich, Germany

Abstract: 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, Fingleton and Pirotte (2014) and Fingleton (2008). 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. (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
Date: 2018-04-18
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-geo, nep-ore and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Related works:
Journal Article: A time-space dynamic panel data model with spatial moving average errors (2019)
Working Paper: A time-space dynamic panel data model with spatial moving average errors (2019)
Working Paper: A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors (2018)
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