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

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  • Girardin, Eric
  • Kholodilin, Konstantin A.

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 helps substantially 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 horizon).

Suggested Citation

  • Girardin, Eric & Kholodilin, Konstantin A., 2010. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," BOFIT Discussion Papers 15/2010, Bank of Finland, Institute for Economies in Transition.
  • Handle: RePEc:bof:bofitp:2010_015
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    Cited by:

    1. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2018. "A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors," MPRA Paper 86371, University Library of Munich, Germany.
    2. 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.
    3. 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.
    4. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers CWP41/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, C.E.P.R. Discussion Papers.
    6. Semerikova, Elena & Demidova, Olga, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 43, pages 29-51.
    7. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, Elsevier.
    8. repec:ura:ecregj:v:1:y:2017:i:2:p:410-421 is not listed on IDEAS

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