关于马尔可夫区制切换模型的贝叶斯滤波
On Bayesian Filtering for Markov Regime Switching Models
AI总结：
本文提出基于贝叶斯滤波的动态宏观经济模型实证分析框架，构建IMM和GPB多区制滤波器与平滑器，证明标准IMM滤波器在精度和速度上优于同类，并在美国数据中成功识别重大政策转变。
摘要：
本文提出了一个使用贝叶斯滤波进行动态宏观经济模型实证分析的框架，特别关注具有多种区制的动态随机一般均衡（DSGE）模型的状态空间表述。我们概述了模型估计的理论基础，提供了两种强大的多区制滤波器族IMM和GPB的细节，并构建了相应的多区制平滑器。基于典型的新凯恩斯主义DSGE模型的模拟练习，用于证明所提出的滤波器和平滑器的计算稳健性，并评估每个族中选定滤波器的准确性和速度。我们表明，标准的IMM滤波器比IMM和GPB族中的竞争者更快，且准确度不低于、通常还高于它们，后者包括常用的Kim和Nelson（1999）滤波器。将其与匹配的平滑器一起使用，可将恢复未观测变量的精度提高约25%。此外，将其应用于美国1947-2023年宏观经济时间序列，我们成功识别了重大的过去政策转变，包括与后新冠时期相关的转变。我们的结果证明了所提出的程序在宏观经济分析中的实用适用性和潜力。
英文摘要：
This paper presents a framework for empirical analysis of dynamic macroeconomic models using Bayesian filtering, with a specific focus on the state-space formulation of Dynamic Stochastic General Equilibrium (DSGE) models with multiple regimes. We outline the theoretical foundations of model estimation, provide the details of two families of powerful multiple-regime filters, IMM and GPB, and construct corresponding multiple-regime smoothers. A simulation exercise, based on a prototypical New Keynesian DSGE model, is used to demonstrate the computational robustness of the proposed filters and smoothers and evaluate their accuracy and speed for a selection of filters from each family. We show that the canonical IMM filter is faster and is no less, and often more, accurate than its competitors within IMM and GPB families, the latter including the commonly used Kim and Nelson (1999) filter. Using it with the matching smoother improves the precision in recovering unobserved variables by about 25 percent. Furthermore, applying it to the U.S. 1947-2023 macroeconomic time series, we successfully identify significant past policy shifts including those related to the post-Covid-19 period. Our results demonstrate the practical applicability and potential of the proposed routines in macroeconomic analysis.
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