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Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models

Florian Huber, Gregor Kastner and Martin Feldkircher

No 2018-5, Working Papers in Economics from University of Salzburg

Abstract: We propose a straightforward algorithm to estimate large Bayesian time-varying parameter vector autoregressions with mixture innovation components for each coefficient in the system. The computational burden becomes manageable by approximating the mixture indicators driving the time-variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. Two applications illustrate the merits of our approach. First, we forecast the US term structure of interest rates and demonstrate forecast gains relative to benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time-varying effects of a monetary policy tightening

Keywords: Time-varying parameter vector autoregression with stochastic volatility (TVP-VARSV); Change point model; Structural breaks; Term structure of interest rates; Monetary policy; R package threshtvp (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-cba, nep-mac, nep-mon and nep-ore
Date: 2018-11-07
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Related works:
Journal Article: Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models (2019) 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
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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