Should I Stay or Should I Go? Bayesian Inference in the Threshold Time Varying Parameter (TTVP) Model

Florian Huber, Gregor Kastner, Martin Feldkircher

Publication: Working/Discussion PaperWU Working Paper

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Abstract

Incorporating structural changes into time series models is crucial during turbulent economic periods. In this paper, we propose a flexible means of estimating vector autoregressions with time-varying parameters (TVP-VARs) by introducing a threshold process that is driven by the absolute size of parameter changes. This enables us to detect whether a given regression coefficient is constant or time-varying. When applied to a medium-scale macroeconomic US dataset our model yields precise density and turning point predictions, especially during economic downturns, and provides new insights on the changing effects of increases in short-term interest rates over time.
Original languageEnglish
Publication statusPublished - 2017

Publication series

NameResearch Report Series / Department of Statistics and Mathematics
No.130

Bibliographical note

Earlier version

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 101018 Statistics
  • 502025 Econometrics
  • 101026 Time series analysis

WU Working Paper Series

  • Research Report Series / Department of Statistics and Mathematics

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