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How helpful are spatial effects in forecasting the growth of Chinese provinces?

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  • Eric Girardin
  • Konstantin A. Kholodilin

Abstract

In this paper, we make multi-step forecasts of the annual growth rates of the real gross regional product (GRP) for each of the 31 Chinese provinces simultaneously. Beside the usual panel data models, we use panel models that explicitly account for spatial dependence between the GRP growth rates. In addition, the possibility of spatial effects being different for different groups of provinces (Interior and Coast) is allowed for. We find that both pooling and accounting for spatial effects help substantially to improve the forecast performance compared to the benchmark models estimated for each of the provinces separately. It is also shown that the effect of accounting for spatial dependence is even more pronounced at longer forecasting horizons (the forecast accuracy gain as measured by the root mean squared forecast error is about 8% at the 1‐year horizon and exceeds 25% at the 13‐ and 14‐year horizons). Copyright (C) 2010 John Wiley & Sons, Ltd.

Suggested Citation

  • Eric Girardin & Konstantin A. Kholodilin, 2011. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 622-643, November.
    • Handle: RePEc:jof:jforec:v:30:y:2011:i:7:p:622-643
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    File URL: http://hdl.handle.net/10.1002/for.1193
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    Cited by:

    1. Raffaella Giacomini, 2015. "Economic theory and forecasting: lessons from the literature," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 22-41, June.
    2. Wozniak Marcin, 2020. "Forecasting the unemployment rate over districts with the use of distinct methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-20, April.
    3. Valerij Gamukin, 2017. "Structural Change of Gross Regional Product in the Subjects of Ural Federal District," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 410-421.
    4. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    5. Elena Semerikova & Olga Demidova, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 29-51.
    6. Andrey V. Polbin & Andrey V. Shumilov, 2022. "Forecasting Output Growth of Russian Manufacturing Industries Using Panel Data Models [Об Использовании Моделей Панельных Данных Для Прогнозирования Темпов Роста Отраслей Российской Обрабатывающей Промышленности]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 2, pages 15-19, February.
    7. Yuan Yang & Junjie Zhang & Can Wang, 2018. "Forecasting China’s Carbon Intensity: Is China on Track to Comply with Its Copenhagen Commitment?," The Energy Journal, , vol. 39(2), pages 63-86, March.
    8. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    9. 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.
    10. Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023. "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 530-551, October.
    11. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    12. Nina Vujanovic & Bruno Casella & Richard Bolwijn, . "Forecasting global FDI: a panel data approach," UNCTAD Transnational Corporations Journal, United Nations Conference on Trade and Development.
    13. Baltagi, Badi H. & Pirotte, Alain, 2014. "Prediction in a spatial nested error components panel data model," International Journal of Forecasting, Elsevier, vol. 30(3), pages 407-414.
    14. Andrey V. Polbin & Andrey V. Shumilov, 2022. "Об Использовании Моделей Панельных Данных Для Прогнозирования Темпов Роста Отраслей Российской Обрабатывающей Промышленности," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 2, pages 15-19, February.

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