Should I stay or should I go? Bayesian inference in the threshold time varying parameter (TTVP) model

Huber, Florian ORCID: and Kastner, Gregor and Feldkircher, Martin ORCID: (2016) Should I stay or should I go? Bayesian inference in the threshold time varying parameter (TTVP) model. Department of Economics Working Paper Series, 235. WU Vienna University of Economics and Business, Vienna.


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We provide a flexible means of estimating time-varying parameter models in a Bayesian framework. By specifying the state innovations to be characterized trough a threshold process that is driven by the absolute size of parameter changes, our model detects at each point in time whether a given regression coefficient is constant or time-varying. Moreover, our framework accounts for model uncertainty in a data-based fashion through Bayesian shrinkage priors on the initial values of the states. In a simulation, we show that our model reliably identifies regime shifts in cases where the data generating processes display high, moderate, and low numbers of movements in the regression parameters. Finally, we illustrate the merits of our approach by means of two applications. In the first application we forecast the US equity premium and in the second application we investigate the macroeconomic effects of a US monetary policy shock.

Item Type: Paper
Keywords: Change point model / Threshold mixture innovations / Structural breaks / Shrinkage / Bayesian statistics / Monetary policy
Classification Codes: JEL C11, C32, C52, E42
Divisions: Departments > Volkswirtschaft
Depositing User: Claudia Tering-Raunig
Date Deposited: 19 Sep 2016 11:42
Last Modified: 07 May 2021 16:29


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