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结构突变与异常值下时变参数及多过程混合模型的蒙特卡洛比较AI 解读

This Monte Carlo study compares the performance of a recently proposed multiprocess mixture model and a more traditional random walk time-varying parameter model in the face of structural shifts and outliers. The mixture model performs well and the latter model performs poorly. This finding is of general interest since investigators often adopt random-walk time-varying parameter models to accommodate potential regime shifts in regression relationships. The findings suggest…

预测组合的混合模型方法AI 解读

A multiprocess mixture-model approach to combining forecasts from alternative sources is proposed. This approach extends the Granger-Ramanathan method by allowing the weights used in producing the combination forecast to vary over time. In addition, the procedure discounts outlying data points that arise during time periods when all of the competing forecasts miss the mark. An empirical comparison with traditional and more recently proposed.combination methods demonstrates…