How helpful are spatial effects in forecasting the growth of Chinese provinces?
AbstractIn 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.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 30 (2011)
Issue (Month): 7 (November)
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
Chinese provinces ; forecasting ; dynamic panel model ; spatial autocorrelation ; group‐specific spatial dependence ;
Other versions of this item:
- 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.
- 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; Longitudinal Data; Spatial Time Series
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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- 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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