MODEL SELECTION FOR FORECASTING
MODEL SELECTION FOR FORECASTING
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DOI：
10.1016/0096-3003(86)90009-3
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发表时间：
1986-11-01
影响因子：
4
通讯作者：
BROWN, SJ
中科院分区：
文献类型：
--
作者：
ENGLE, RF;BROWN, SJ
本文提出了实证比较的预测精度所产生的各种模型选择程序。模型的住宅电力的月销售额估计，并用于预测三年到未来的20个州在美国的模型是由各种复杂性标准和向上和向下的F-检验在各种显着性水平。预测精度用一步和多步条件均方根预测误差来衡量。总体而言，最严重的惩罚overparametrized模型表现最好的选择标准：施瓦茨标准和1%大小序贯F-检验。
This paper presents empirical comparisons of forecast accuracy resulting from variety of model selection procedures. Models of monthly sales of residential electricity are estimated, and used to forecast three years into the future for twenty states in the U.S. Models are selected by a variety of complexity criteria and by upward and downwardF-tests at various significance levels. Forecast accuracy was measured by one-step and multistep conditional root-mean-square forecast errors. Overall the selection criteria which most heavily penalized overparametrized models performed best: the Schwarz criterion and 1% size sequentialF-testing.