Model selection for multiperiod forecasts
Model selection for multiperiod forecasts
复制标题
多期预测的模型选择
DOI：
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发表时间：
1996
期刊：
影响因子：
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通讯作者：
Shu
中科院分区：
文献类型：
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作者：
Shu
研究了基于联合多周期预测的时间序列数据模型选择问题。将一些流行的选择标准应用到合适的多元回归模型中，以创建新的选择标准。蒙特卡罗实验表明，所提出的选择过程比相应的常规过程更有效，特别是当数据来自高阶自回归模型时。对一些实际数据的分析支持了所建议程序的适用性，特别是当所需的差异程度不确定时。
SUMMARY Model selection for time series data based upon joint multiperiod forecasts is investigated. Some popular selection criteria are applied to a suitable multivariate regression model to create new selection criteria. Monte Carlo experiments show that the proposed selection procedure performs more efficiently than the corresponding regular procedure, particularly when the data are generated from a high order autoregressive model. Analysis of some real data supports the applicability of the proposed procedure, especially when the degree of differencing required is uncertain.