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

Referierte Aufsätze Web of Science

Eric Girardin, Konstantin A. Kholodilin

In: Journal of Forecasting 30 (2011), 7, S. 622-643


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 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).

Konstantin A. Kholodilin

Research Associate in the Macroeconomics Department

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