Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach

Richard T. Baille and Claudio Morana

ICER Working Papers - Applied Mathematics Series from ICER - International Centre for Economic Research

Abstract: Previous models of monthly CPI inflation time series have focused on possible regime shifts, non-linearities and the feature of long memory. This paper proposes a new time series model, named Adaptive ARFIMA; which appears well suited to describe inflation and potentially other economic time series data. The Adaptive ARFIMA model includes a time dependent intercept term which follows a Flexible Fourier Form. The model appears to be capable of succesfully dealing with various forms of breaks and discontinities in the conditional mean of a time series. Simulation evidence justifies estimation by approximate MLE and model specfication through robust inference based on QMLE. The Adaptive ARFIMA model when supplemented with conditional variance models is found to provide a good representation of the G7 monthly CPI inflation series.

Keywords: ARFIMA; FIGARCH, long memory, structural change, inflation, G7. (search for similar items in EconPapers)
JEL-codes: C15 C22 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2009-05
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets, nep-mac and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:icr:wpmath:06-2009

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