Aggregation of space-time processes
Aggregation of space-time processes
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DOI：
10.1016/s0304-4076(03)00132-5
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发表时间：
2004-01-01
影响因子：
6.3
通讯作者：
Granger, CWJ
中科院分区：
文献类型：
--
作者：
Giacomini, R;Granger, CWJ
在这篇文章中，我们比较了预测空间相关变量集合的不同方法的相对效率。小样本仿真证实了渐近结果，即通过对系统中的空间相关量施加先验约束，可以获得更好的预测性能。要做到这一点，一种方法是从时空自回归模型(空间结构元素，剑桥大学出版社，剑桥，1975)中汇总预测，该模型提供了解决使用VAR进行预测时出现的“维度诅咒”的解决方案。我们还表明，忽视空间相关性，即使空间相关性很弱，也会导致高度不准确的预测。最后，如果系统满足“池化”条件，则直接预测总变量是有好处的。(C)2003爱思唯尔B.V.保留所有权利。
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. (C) 2003 Elsevier B.V. All rights reserved.