Long memory, economic policy uncertainty and forecasting US inflation : a Bayesian VARFIMA approach
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Date
Authors
Balcilar, Mehmet
Gupta, Rangan
Jooste, Charl
Journal Title
Journal ISSN
Volume Title
Publisher
Routledge
Abstract
We compare inflation forecasts of a vector fractionally integrated autoregressive
moving average (VARFIMA) model against standard forecasting models. U.S.
inflation forecasts improve when controlling for persistence and economic policy
uncertainty (EPU). Importantly, the VARFIMA model, comprising of inflation and
EPU, outperforms commonly used inflation forecast models.
Description
Keywords
Inflation, Long-range dependency, Vector fractionally integrated autoregressive moving average (VARFIMA), Economic policy uncertainty (EPU)
Sustainable Development Goals
Citation
Mehmet Balcilar, Rangan Gupta & Charl Jooste (2017) Long memory,
economic policy uncertainty and forecasting US inflation: a Bayesian VARFIMA approach, Applied
Economics, 49:11, 1047-1054, DOI:
10.1080/00036846.2016.1210777.