Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces?
AbstractIn 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).
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Bibliographic InfoPaper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 938.
Length: 32 p.
Date of creation: 2009
Date of revision:
Chinese provinces; forecasting; dynamic panel model; spatial autocorrelation; group-specific spatial dependence;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-11-14 (All new papers)
- NEP-DEV-2009-11-14 (Development)
- NEP-FOR-2009-11-14 (Forecasting)
- NEP-GEO-2009-11-14 (Economic Geography)
- NEP-URE-2009-11-14 (Urban & Real Estate Economics)
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- You, Jing, 2013. "China's challenge for decarbonized growth: Forecasts from energy demand models," Journal of Policy Modeling, Elsevier, Elsevier, vol. 35(4), pages 652-668.
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