A time-varying threshold STAR model with applications Article Swipe

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Michael J. Dueker , Laura E. Jackson , Michael T. Owyang , Martín Solà ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1093/ooec/odac012

Smooth-transition autoregressive (STAR) models, competitors of Markov-switching models, are limited by an assumed time-invariant threshold level. We augment the STAR model with a time-varying threshold that can be interpreted as a ‘tipping level’ where the mean and dynamics of the VAR shift. Thus, the time-varying latent threshold level serves as a demarcation between regimes. We show how to estimate the model in a Bayesian framework using a Metropolis step and an unscented Kalman filter proposal. To show how allowing time variation in the threshold can affect the results, we present two applications: a model of the natural rate of unemployment and a model of regime-dependent government spending.

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Autoregressive model STAR model Threshold model Kalman filter Econometrics Bayesian probability Star (game theory) Markov chain Invariant (physics) Markov model Mathematics Statistical physics Computer science Statistics Autoregressive integrated moving average Time series Physics Mathematical analysis Mathematical physics

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Language: en
Landing Page: https://doi.org/10.1093/ooec/odac012
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Title: A time-varying threshold STAR model with applications

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Publication year: 2022

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Authors: Michael J. Dueker, Laura E. Jackson, Michael T. Owyang, Martín Solà

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Concepts: Autoregressive model, STAR model, Threshold model, Kalman filter, Econometrics, Bayesian probability, Star (game theory), Markov chain, Invariant (physics), Markov model, Mathematics, Statistical physics, Computer science, Statistics, Autoregressive integrated moving average, Time series, Physics, Mathematical analysis, Mathematical physics

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