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Forecasting time series of economic processes by model averaging across data frames of various lengths

机译：通过对各种长度的数据帧进行模型平均来预测经济过程的时间序列

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This paper presents an extension of mean-squared forecast error (MSFE) model averaging for integrating linear regression models computed on data frames of various lengths. Proposed method is considered to be a preferable alternative to best model selection by various efficiency criteria such as Bayesian information criterion (BIC), Akaike information criterion (AIC), F-statistics and mean-squared error (MSE) as well as to Bayesian model averaging (BMA) and naive simple forecast average. The method is developed to deal with possibly non-nested models having different number of observations and selects forecast weights by minimizing the unbiased estimator of MSFE. Proposed method also yields forecast confidence intervals with a given significance level what is not possible when applying other model averaging methods. In addition, out-of-sample simulation and empirical testing proves efficiency of such kind of averaging when forecasting economic processes.
机译：本文提出了均方预测误差（MSFE）模型平均的扩展，用于集成在各种长度的数据帧上计算的线性回归模型。通过各种效率标准（例如贝叶斯信息标准（BIC），Akaike信息标准（AIC），F统计量和均方误差（MSE））以及贝叶斯模型，该方法被认为是最佳模型选择的首选替代方法平均（BMA）和简单的简单预测平均值。开发该方法以处理具有不同观察次数的可能非嵌套模型，并通过最小化MSFE的无偏估计量来选择预测权重。所提出的方法还可以产生具有给定显着性水平的预测置信区间，而这在应用其他模型平均方法时是不可能的。此外，样本外仿真和经验测试证明了在预测经济过程时这种平均的效率。

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