Aggregation of space-time processes
In this paper we compare the relative efficiency of different methods of forecasting the aggregate of spatially correlated variables. Small sample simulations confirm the asymptotic result that improved forecasting performance can be obtained by imposing a priori constraints on the amount of spatial...
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| Veröffentlicht in: | Journal of econometrics 2004, Vol.118 (1), p.7-26 |
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| container_title | Journal of econometrics |
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| creator | Giacomini, Raffaella Granger, Clive W.J. |
| description | In this paper we compare the relative efficiency of different methods of forecasting the aggregate of spatially correlated variables. Small sample simulations confirm the asymptotic result that improved forecasting performance can be obtained by imposing a priori constraints on the amount of spatial correlation in the system. One way to do so is to aggregate forecasts from a space-time autoregressive model (Elements of Spatial Structure, Cambridge University Press, Cambridge, 1975), which offers a solution to the ‘curse of dimensionality’ that arises when forecasting with VARs. We also show that ignoring spatial correlation, even when it is weak, leads to highly inaccurate forecasts. Finally, if the system satisfies a ‘poolability’ condition, there is a benefit in forecasting the aggregate variable directly. |
| doi_str_mv | 10.1016/S0304-4076(03)00132-5 |
| format | Article |
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| identifier | ISSN: 0304-4076 |
| ispartof | Journal of econometrics, 2004, Vol.118 (1), p.7-26 |
| issn | 0304-4076 1872-6895 |
| language | eng |
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| source | RePEc; Access via ScienceDirect (Elsevier) |
| subjects | Aggregation Correlation analysis Econometric models Econometrics Economic forecasting Economic methodology Economic models Forecast efficiency Mathematical economics Regression analysis Space Space-time models Spacetime Spatial correlation Studies Time Time series VAR |
| title | Aggregation of space-time processes |
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