Large Vector Auto Regressions
Large Vector Auto Regressions
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DOI：
10.18452/4336
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发表时间：
2011-06
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影响因子：
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通讯作者：
Song Song-Song;P. Bickel
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作者：
Song Song-Song;P. Bickel
非结构性经济和金融预测的一种流行方法是包括大量的经济和金融变量，这已被证明会导致预测的显着改进，例如，通过动态因子模型。一个具有挑战性的问题是确定哪些变量和（它们的）滞后是相关的，特别是当存在序列相关性（时间动态）、高维（空间）依赖结构和中等样本大小（相对于维度和滞后）的混合时。为此，同时解决这三个挑战的集成解决方案很有吸引力。我们在这里用三种类型的估计来研究大向量自回归。我们对待每个变量的滞后不同于其他变量的滞后，区分不同的滞后随着时间的推移，并能够同时选择变量和滞后。我们首先显示的后果，直接使用Lasso型估计的时间序列，而不考虑时间依赖。相比之下，我们提出的方法仍然可以在这种情况下产生像预言一样有效的估计。通过数据驱动的“滚动方案”方法选择调整参数，以优化预测性能。一个宏观经济和金融预测问题被认为是说明其优于现有的估计。
One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their) lags are relevant, especially when there is a mixture of serial correlation (temporal dynamics), high dimensional (spatial) dependence structure and moderate sample size (relative to dimensionality and lags). To this end, an integrated solution that addresses these three challenges simultaneously is appealing. We study the large vector auto regressions here with three types of estimates. We treat each variable's own lags different from other variables' lags, distinguish various lags over time, and is able to select the variables and lags simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal dependence. In contrast, our proposed method can still produce an estimate as efficient as an oracle under such scenarios. The tuning parameters are chosen via a data driven "rolling scheme" method to optimize the forecasting performance. A macroeconomic and financial forecasting problem is considered to illustrate its superiority over existing estimators.