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 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).
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Bank of Finland, Institute for Economies in Transition in its series BOFIT Discussion Papers with number 15/2010.
Length: 39 pages
Date of creation: 23 Aug 2010
Date of revision:
Contact details of provider:
Postal: Bank of Finland, BOFIT, P.O. Box 160, FI-00101 Helsinki, Finland
Phone: + 358 10 831 2268
Fax: + 358 10 831 2294
Web page: http://www.suomenpankki.fi/bofit_en/
More information through EDIRC
Chinese provinces; forecasting; dynamic panel model; spatial autocorrelation; group-specific spatial dependence;
Other versions of this item:
- Eric Girardin & Konstantin A. Kholodilin, 2011. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 622-643, November.
- 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-2010-09-03 (All new papers)
- NEP-FOR-2010-09-03 (Forecasting)
- NEP-GEO-2010-09-03 (Economic Geography)
- NEP-URE-2010-09-03 (Urban & Real Estate Economics)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Päivi Määttä).
If references are entirely missing, you can add them using this form.