APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs
针对多国面板向量自回归模型参数过多的问题，提出快速贝叶斯方法，利用旋转高斯近似和马尔可夫链蒙特卡洛技术分组估计国内外信息，并在世界经济模型中验证了预测效率。
Abstract Panel vector autoregressions (PVARs) are a popular tool for analyzing multicountry data sets. However, the number of estimated parameters can be enormous, leading to computational and statistical issues. In this article, we develop fast Bayesian methods for estimating PVARs using integrated rotated Gaussian approximations. We exploit the fact that domestic information is often more important than international information and group the coefficients accordingly. Fast approximations are used to estimate the latter whereas the former are estimated with precision using Markov chain Monte Carlo techniques. We illustrate, using a huge model of the world economy, that it produces competitive forecasts quickly.