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Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces?

Author

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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 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. 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 was also shown that 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 1-year horizon and exceeds 25% at 13- and 14-year horizon).

Suggested Citation

  • Eric Girardin & Konstantin A. Kholodilin, 2009. "Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces?," Discussion Papers of DIW Berlin 938, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp938
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    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.342414.de/dp938.pdf
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    Citations

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    Cited by:

    1. Hao, Yu & Zhang, Zong-Yong & Liao, Hua & Wei, Yi-Ming, 2015. "China’s farewell to coal: A forecast of coal consumption through 2020," Energy Policy, Elsevier, vol. 86(C), pages 444-455.
    2. You, Jing, 2013. "China's challenge for decarbonized growth: Forecasts from energy demand models," Journal of Policy Modeling, Elsevier, vol. 35(4), pages 652-668.
    3. Jiandong Ju & Li Su, 2013. "Market structure in the Chinese steel industry," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 20(1), pages 70-84, March.

    More about this item

    Keywords

    Chinese provinces; forecasting; dynamic panel model; spatial autocorrelation; group-specific spatial dependence;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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