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A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations
Author(s) -
LANNE MARKKU,
LUOTO JANI
Publication year - 2017
Publication title -
journal of money, credit and banking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.763
H-Index - 108
eISSN - 1538-4616
pISSN - 0022-2879
DOI - 10.1111/jmcb.12402
Subject(s) - autoregressive model , univariate , phillips curve , econometrics , inflation (cosmology) , volatility (finance) , new keynesian economics , star model , stochastic volatility , economics , constant (computer programming) , autoregressive integrated moving average , mathematics , keynesian economics , time series , multivariate statistics , monetary policy , statistics , computer science , physics , theoretical physics , programming language
We study the evolution of U.S. inflation by means of a new noncausal autoregressive model with time‐varying parameters that outperforms the corresponding causal and constant‐parameter noncausal models in terms of fit and forecast accuracy. Our model also beats the unobserved component stochastic volatility (UCSV) model, one of the best‐performing univariate inflation forecasting models, in terms of both point and density forecasts. We also show how the new Keynesian Phillips curve can be estimated based on our noncausal model. Both expected and lagged inflation turn out important, but the former dominates in determining the current inflation.