多维多国VAR模型的近似贝叶斯推理与预测
Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs
摘要：
面板向量自回归（PVAR）是分析多国数据集的常用工具。然而，估计参数的数量可能是巨大的，导致计算和统计问题。在本文中，我们开发了快速贝叶斯方法估计PVAR使用集成旋转高斯近似。我们利用国内信息往往比国际信息更重要的事实，并相应地对系数进行分组。快速近似被用来估计后者，而前者估计精度使用马尔可夫链蒙特卡罗技术。我们使用一个巨大的世界经济模型来说明，它可以快速产生有竞争力的预测。
英文摘要：
Panel Vector Autoregressions (PVARs) are a popular tool for analyzing multi-country datasets. However, the number of estimated parameters can be enormous, leading to computational and statistical issues. In this paper, 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 while 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
