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

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  • Baltagi, Badi H.

    (Syracuse University)

  • Fingleton, Bernard

    (University of Cambridge)

  • Pirotte, Alain

    (University of Paris 2)

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.

Suggested Citation

  • Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2018. "A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors," IZA Discussion Papers 11587, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp11587
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    4. Bernard Fingleton, 2020. "Exploring Brexit with dynamic spatial panel models: some possible outcomes for employment across the EU regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 455-491, April.
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    6. Bernard Fingleton & Daniel Olner & Gwilym Pryce, 2020. "Estimating the local employment impacts of immigration: A dynamic spatial panel model," Urban Studies, Urban Studies Journal Limited, vol. 57(13), pages 2646-2662, October.
    7. Xinghua Wang & Shunchen Wu & Xiaojuan Qin & Meixiang La & Haixia Zuo, 2022. "Informal Environment Regulation, Green Technology Innovation and Air Pollution: Quasi-Natural Experiments from Prefectural Cities in China," Sustainability, MDPI, vol. 14(10), pages 1-13, May.
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    9. Bernard Fingleton, 2020. "Italexit, is it another Brexit?," Journal of Geographical Systems, Springer, vol. 22(1), pages 77-104, January.
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    13. Yue Wang & Lei Shi & Di Chen & Xue Tan, 2020. "Spatial-Temporal Analysis and Driving Factors Decomposition of (De)Coupling Condition of SO 2 Emissions in China," IJERPH, MDPI, vol. 17(18), pages 1-18, September.
    14. Liu, Yunqiang & Zhu, Jialing & Li, Eldon Y. & Meng, Zhiyi & Song, Yan, 2020. "Environmental regulation, green technological innovation, and eco-efficiency: The case of Yangtze river economic belt in China," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
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    More about this item

    Keywords

    prediction; moving average; direct and indirect effects; spatial autocorrelation; GM; within; OLS; dynamic; time-space; error components; spatial lag; panel data; simulations; rook contiguity; interregional trade;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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