Relevant parameter changes in structural break models
Arnaud Dufays and
Jeroen V.K. Rombouts
Journal of Econometrics, 2020, vol. 217, issue 1, 46-78
Structural break time series models, which are commonly used in macroeconomics and finance, capture unknown structural changes by allowing for abrupt changes to model parameters. However, many specifications suffer from an over-parametrization issue, since typically all parameters have to change when a break occurs. We introduce a sparse change-point model to detect which parameters change over time. We propose a shrinkage prior distribution, which controls model parsimony by limiting the number of parameters that change from one structural break to another. We develop a Bayesian sampler for inference on the sparse change-point model. An extensive simulation study based on AR, ARMA and GARCH processes highlights the excellent performance of the sampler. We provide several empirical applications including an out-of-sample forecasting exercise showing that the Sparse change-point framework compares favorably with other recent time-varying parameter processes.
Keywords: Shrinkage prior; Structural break model; Relevant parameter change; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C58 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:217:y:2020:i:1:p:46-78
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