标题： 大贝叶斯向量自回归模型用于二元和删失变量 显示英文标题

标题： Large Bayesian VARs for Binary and Censored Variables

摘要： 我们将标准向量自回归（VAR）扩展到联合建模二元变量、截断变量和连续变量的动力学，并开发了一种在高维设置下表现良好的高效估计方法。 在样本外预测练习中，我们展示了所提出的向量自回归模型能够很好地预测经济衰退和短期利率。 我们通过广泛的实证应用展示了所提出框架的实用性，包括条件预测以及一项结构分析，该分析考察了金融冲击对经济衰退概率的动态影响。 显示英文摘要

摘要： We extend the standard VAR to jointly model the dynamics of binary, censored and continuous variables, and develop an efficient estimation approach that scales well to high-dimensional settings. In an out-of-sample forecasting exercise, we show that the proposed VARs forecast recessions and short-term interest rates well. We demonstrate the utility of the proposed framework using a wide rage of empirical applications, including conditional forecasting and a structural analysis that examines the dynamic effects of a financial shock on recession probabilities.