FORECASTING LARGE DATASETS WITH BAYESIAN REDUCED RANK MULTIVARIATE MODELS
FORECASTING LARGE DATASETS WITH BAYESIAN REDUCED RANK MULTIVARIATE MODELS
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DOI：
10.1002/jae.1150
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发表时间：
2011-08-01
影响因子：
2.1
通讯作者：
Marcellino, Massimiliano
中科院分区：
文献类型：
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作者：
Carriero, Andrea;Kapetanios, George;Marcellino, Massimiliano
本文讨论了使用多变量模型来预测一大组变量的问题。特别是，我们提出了三种替代降阶预测模型，并将它们对美国时间序列的预测性能与现有的最有前景的替代模型，即因子模型、大规模贝叶斯VAR和多元提升进行了比较。具体地说，我们关注经典的降阶回归，这是一个两步过程，依次应用收缩和降阶限制，以及Geweke(1996)的降阶贝叶斯VAR。我们发现，无论是对整个变量集进行预测，还是对工业生产增长、通货膨胀和联邦基金利率等关键变量进行预测，联合使用收缩和降阶都大大提高了预测的准确性。基于自举数据的蒙特卡罗实验证实了这一发现的稳健性。我们还给出了当系统的维度趋于无穷大时降阶回归有效的一致性结果，为大规模降阶模型用于实证分析铺平了道路。版权所有(C)2010 John Wiley&Sons，Ltd.
The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance for US time series with the most promising existing alternatives, namely, factor models, large-scale Bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two-step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank Bayesian VAR of Geweke (1996). We find that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast and for key variables such as industrial production growth, inflation, and the federal funds rate. The robustness of this finding is confirmed by a Monte Carlo experiment based on bootstrapped data. We also provide a consistency result for the reduced rank regression valid when the dimension of the system tends to infinity, which opens the way to using large-scale reduced rank models for empirical analysis. Copyright (C) 2010 John Wiley & Sons, Ltd.