We develop a flexible framework for Bayesian estimation of impulse responses using Local Projections (LPs) with instrumental variables. It accommodates multiple shocks and instruments, accounts for autocorrelation in multi-step forecasts by jointly modeling all LPs as a seemingly unrelated system of equations, defines a flexible yet parsimonious joint prior for impulse responses based on a Gaussian Process, and allows for joint inference about the entire vector of impulse responses. We show via Monte Carlo simulations that our approach delivers more accurate point and uncertainty estimates than standard methods. To address misspecification, we propose an optional robustification step based on power posteriors.

翻译：我们开发了一个灵活的贝叶斯框架，用于使用工具变量的局部投影法估计脉冲响应。该框架能够处理多个冲击和工具变量，通过将所有局部投影模型联合建模为一个似不相关方程组，解决了多步预测中的自相关问题；基于高斯过程定义了灵活且简约的脉冲响应联合先验分布，并允许对整个脉冲响应向量进行联合推断。蒙特卡洛模拟表明，相比标准方法，我们的方法能提供更精确的点估计和不确定性估计。为应对模型设定偏误，我们提出了基于幂后验的可选稳健化步骤。