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FORECASTING US INFLATION USING DYNAMIC GENERAL‐TO‐SPECIFIC MODEL SELECTION
Author(s) -
Bagdatoglou George,
Kontonikas Alexandros,
Wohar Mark E.
Publication year - 2016
Publication title -
bulletin of economic research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 29
eISSN - 1467-8586
pISSN - 0307-3378
DOI - 10.1111/boer.12041
Subject(s) - univariate , inflation (cosmology) , econometrics , economics , benchmark (surveying) , model selection , selection (genetic algorithm) , consensus forecast , set (abstract data type) , computer science , multivariate statistics , machine learning , physics , theoretical physics , programming language , geography , geodesy
We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out‐of‐sample forecasts are generated from models identified from a multipath general‐to‐specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well‐established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods.