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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

Papers from arXiv.org

Abstract: This paper proposes a straightforward algorithm to carry out inference in large time-varying parameter vector autoregressions (TVP-VARs) with mixture innovation components for each coefficient in the system. We significantly decrease the computational burden by approximating the latent indicators that drive the time-variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. The merits of our approach are illustrated with two applications. First, we forecast the US term structure of interest rates and demonstrate forecast gains of the proposed mixture innovation model relative to other benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time-varying effects of a monetary policy tightening.

New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
Date: 2016-07, Revised 2018-07
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http://arxiv.org/pdf/1607.04532 Latest version (application/pdf)

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? 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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