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Forecasting US inflation using Markov dimension switching
Author(s) -
Prüser Jan
Publication year - 2021
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2723
Subject(s) - bernoulli's principle , context (archaeology) , inflation (cosmology) , econometrics , markov chain , dimension (graph theory) , bayesian probability , set (abstract data type) , computer science , variable (mathematics) , mathematics , artificial intelligence , machine learning , paleontology , mathematical analysis , physics , theoretical physics , pure mathematics , engineering , biology , programming language , aerospace engineering
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.