VAR FORECASTING USING BAYESIAN VARIABLE SELECTION
开发了在贝叶斯向量自回归中自动选择变量的方法，使用吉布斯抽样实现高效计算，并在英国三大宏观经济时间序列预测中验证了其优于无约束模型和收缩估计。
SUMMARY This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic linear and nonlinear models, as well as models of large dimensions. The performance of the proposed variable selection method is assessed in forecasting three major macroeconomic time series of the UK economy. Data‐based restrictions of VAR coefficients can help improve upon their unrestricted counterparts in forecasting, and in many cases they compare favorably to shrinkage estimators. Copyright © 2011 John Wiley & Sons, Ltd.