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

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Author Info

  • Girardin , Eric

    ()
    (BOFIT)

  • Kholodilin, Konstantin A.

    (BOFIT)

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).

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Bibliographic Info

Paper provided by Bank of Finland, Institute for Economies in Transition in its series BOFIT Discussion Papers with number 15/2010.

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Length: 39 pages
Date of creation: 23 Aug 2010
Date of revision:
Handle: RePEc:hhs:bofitp:2010_015

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Postal: Bank of Finland, BOFIT, P.O. Box 160, FI-00101 Helsinki, Finland
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Related research

Keywords: Chinese provinces; forecasting; dynamic panel model; spatial autocorrelation; group-specific spatial dependence;

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Cited by:
  1. Konstantin A. Kholodilin & Andreas Mense, 2012. "Forecasting the Prices and Rents for Flats in Large German Cities," Discussion Papers of DIW Berlin 1207, DIW Berlin, German Institute for Economic Research.

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