预测MIDAS模型中的变量选择

Clément Marsilli
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引用次数: 41

摘要

在进行短期预测时,必须考虑到有关当前经济活动状况的所有现有资料。然而,不同时间序列在不同频率下采样的事实阻碍了可用数据的有效利用。在这方面,混合数据采样(MIDAS)模型通过组合不同频率的数据序列,已被证明优于现有工具。然而,关于解释变量的选择仍然存在主要问题。本文首先通过开发基于MIDAS的降维技术和引入两种基于惩罚变量选择方法或贝叶斯随机搜索变量选择方法的新方法来解决这一点。这些特征集成了一个交叉验证程序,允许基于最近预测性能的样本内自动选择。然后利用日数据和月数据对所开发的技术对2000-2013年美国经济增长的预测能力进行了评估。我们的模型成功地识别了领先指标,构建了具有广泛适用性的客观变量选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variable Selection in Predictive MIDAS Models
In short-term forecasting, it is essential to take into account all available information on the current state of the economic activity. Yet, the fact that various time series are sampled at different frequencies prevents an efficient use of available data. In this respect, the Mixed-Data Sampling (MIDAS) model has proved to outperform existing tools by combining data series of different frequencies. However, major issues remain regarding the choice of explanatory variables. The paper first addresses this point by developing MIDAS based dimension reduction techniques and by introducing two novel approaches based on either a method of penalized variable selection or Bayesian stochastic search variable selection. These features integrate a cross-validation procedure that allows automatic in-sample selection based on recent forecasting performances. Then the developed techniques are assessed with regards to their forecasting power of US economic growth during the period 2000-2013 using jointly daily and monthly data. Our model succeeds in identifying leading indicators and constructing an objective variable selection with broad applicability.
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