Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230287
Authors: 
Prüser, Jan
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] [Issue:] [Pages:] n/a-n/a
Abstract: 
This study considers Bayesian variable selection in the Phillips curve context by using the Bernoulli approach of Korobilis (Journal of Applied Econometrics, 2013, 28(2), 204–230). The Bernoulli model, however, is unable to account for model change over time, which is important if the set of relevant predictors changes. To tackle this problem, this paper extends the Bernoulli model by introducing a novel modeling approach called Markov dimension switching (MDS). MDS allows the set of predictors to change over time. It turns out that only a small set of predictors is relevant and that the relevant predictors exhibit a sizable degree of time variation for which the Bernoulli approach is not able to account, stressing the importance and benefit of the MDS approach. In addition, this paper provides empirical evidence that allowing for changing predictors over time is crucial for forecasting inflation.
Subjects: 
fat data
model change
Phillips curve
variable selection
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
Document Version: 
Published Version

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