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

Author

Listed:
  • Baltagi, Badi H.
  • Fingleton, Bernard
  • Pirotte, Alain

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 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.

Suggested Citation

  • Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2019. "A time-space dynamic panel data model with spatial moving average errors," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 13-31.
    • Handle: RePEc:eee:regeco:v:76:y:2019:i:c:p:13-31
    DOI: 10.1016/j.regsciurbeco.2018.04.013
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    Cited by:

    1. 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.
    2. 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).
    3. Bernard Fingleton & Franz Fuerst & Nikodem Szumilo, 2019. "Housing affordability: Is new local supply the key?," Environment and Planning A, , vol. 51(1), pages 25-50, February.
    4. 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.
    5. Carsten Ochsen, 2021. "Age cohort effects on unemployment in the USA: Evidence from the regional level," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1025-1053, August.
    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. 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.
    8. Yingxia Pu & Guangqing Chi & Jin Zhao & Fanhua Kong & Xinyi Zhao, 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.
    9. Fingleton, Bernard & Szumilo, Nikodem, 2019. "Simulating the impact of transport infrastructure investment on wages: A dynamic spatial panel model approach," Regional Science and Urban Economics, Elsevier, vol. 75(C), pages 148-164.
    10. 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.
    11. 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).
    12. Holtermann, Linus & Hundt, Christian, 2018. "Hierarchically structured determinants and phase-related patterns of economic resilience – An empirical case study for European regions," MPRA Paper 88359, University Library of Munich, Germany.
    13. 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.
    14. 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.
    15. Bernard Fingleton, 2023. "Estimating dynamic spatial panel data models with endogenous regressors using synthetic instruments," Journal of Geographical Systems, Springer, vol. 25(1), pages 121-152, January.
    16. 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.
    17. Bernard Fingleton, 2020. "Italexit, is it another Brexit?," Journal of Geographical Systems, Springer, vol. 22(1), pages 77-104, January.

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    JEL classification:

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

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