Bayesian Modeling of Time-varying Parameters Using Regression Trees

简介:

In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian Additive Regression Trees (BART). The novelty of this model arises from the law of motion driving the parameters being treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.

时间:2022-11-16(Wednesday)16:40-18:00
地点:Room N302, Economics Building
会议语言:English
主办单位:厦门大学经济学院、王亚南经济研究院
承办单位:厦门大学经济学院、王亚南经济研究院
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联系人信息:许老师,0592-2182991

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