Forecasting US inflation using Markov dimension switching

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Forecasting ; ISSN: 1099-131X ; Volume: 40 ; Year: 2021 ; Issue: 3 ; Pages: 481-499 ; Hoboken, NJ: Wiley

Classification and Topics

• Subject: fat data ; model change ; Phillips curve ; variable selection

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Prüser, Jan
• Event: Veröffentlichung
• (who): Wiley
• (where): Hoboken, NJ
• (when): 2021

Further information

• DOI: doi:10.1002/for.2723
• Handle: http://hdl.handle.net/10419/230287
• Last update: 10.03.2025, 11:42 AM CET