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

Florian Huber, Gregor Kastner and Martin Feldkircher

Department of Economics Working Papers from Vienna University of Economics and Business, Department of Economics

Abstract: 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 num- bers 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.

Keywords: Change point model; Threshold mixture innovations; Structural breaks; Shrinkage; Bayesian statistics; Monetary policy (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 E42 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-mac and nep-ore
Date: 2016-09
Note: PDF Document
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Related works:
Working Paper: Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models (2018) Downloads
Working Paper: Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models (2018) Downloads
Working Paper: Should I stay or should I go? Bayesian inference in the threshold time varying parameter (TTVP) model (2016) Downloads
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