BOFIT Discussion Papers, Institute for Economies in Transition, Bank of Finland
How helpful are spatial effects in forecasting the growth of Chinese provinces?
() and Konstantin A. Kholodilin
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; (follow links to similar papers)
JEL-Codes: C21; C23; C53; (follow links to similar papers)
39 pages, August 23, 2010
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