How Helpful are Spatial Effects in Forecasting the Growth of Chinese Provinces?

39 Pages Posted: 27 Aug 2010

See all articles by Eric Girardin

Eric Girardin

University Aix-Marseille 2 - GREQAM

Konstantin A. Kholodilin

German Institute for Economic Research (DIW Berlin)

Date Written: August 18, 2010

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).

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

JEL Classification: C21, C23, C53

Suggested Citation

Girardin, Eric and Kholodilin, Konstantin A., How Helpful are Spatial Effects in Forecasting the Growth of Chinese Provinces? (August 18, 2010). BOFIT Discussion Paper No. 15/2010, Available at SSRN: https://ssrn.com/abstract=1665020 or http://dx.doi.org/10.2139/ssrn.1665020

Eric Girardin (Contact Author)

University Aix-Marseille 2 - GREQAM ( email )

Centre de la Vieille Charité
Marseille, 13 002
France

Konstantin A. Kholodilin

German Institute for Economic Research (DIW Berlin) ( email )

Mohrenstraße 58
Berlin, 10117
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
59
Abstract Views
541
rank
441,094
PlumX Metrics