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A time-space dynamic panel data model with spatial moving average errors

Author

Listed:
  • Badi Baltagi

    (Syracuse University)

  • Bernard Fingleton

    (CAM - University of Cambridge [UK])

  • Alain Pirotte

    (CRED - Centre de Recherche en Economie et Droit - UP2 - Université Panthéon-Assas)

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Badi Baltagi & Bernard Fingleton & Alain Pirotte, 2019. "A time-space dynamic panel data model with spatial moving average errors," Post-Print hal-04129306, HAL.
    • Handle: RePEc:hal:journl:hal-04129306
    DOI: 10.1016/j.regsciurbeco.2018.04.013
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    Cited by:

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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.
    5. Linus Holtermann & Christian Hundt, 2018. "Hierarchically structured determinants and phase related patterns of economic resilience. An empirical case study for European regions," Working Papers on Innovation and Space 2018-02, Philipps University Marburg, Department of Geography.
    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.
    8. Mihaela Simionescu & Carmen Beatrice Păuna & Mihaela-Daniela Vornicescu Niculescu, 2021. "The Relationship between Economic Growth and Pollution in Some New European Union Member States: A Dynamic Panel ARDL Approach," Energies, MDPI, vol. 14(9), pages 1-17, April.
    9. Bernard Fingleton, 2020. "Italexit, is it another Brexit?," Journal of Geographical Systems, Springer, vol. 22(1), pages 77-104, January.
    10. Jingjing Li & Yingbin Feng & Lei Gu, 2024. "Telecoupling Effects among Provinces of Cultivated Land Grain Production in the Last 30 Years: Evidence from China," Agriculture, MDPI, vol. 14(7), pages 1-18, July.
    11. Marinos, Theocharis & Belegri-Roboli, Athena & Michaelides, Panayotis G. & Konstantakis, Konstantinos Ν., 2022. "The spatial spillover effect of transport infrastructures in the Greek economy (2000–2013): A panel data analysis," Research in Transportation Economics, Elsevier, vol. 94(C).
    12. Fingleton Bernard & Gardiner Ben & Martin Ron & Barbieri Luca, 2023. "The impact of brexit on regional productivity in the UK," ZFW – Advances in Economic Geography, De Gruyter, vol. 67(2-3), pages 142-160, August.
    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).
    15. Bernard Fingleton, 2022. "Modifying the linear two-step Windmeijer correction for the presence of spatial error dependence," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-18, December.
    16. Yingxia Pu & Xinyi Zhao & Guangqing Chi & Jin Zhao & Fanhua Kong, 2019. "A spatial dynamic panel approach to modelling the space-time dynamics of interprovincial migration flows in China," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(31), pages 913-948.
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