Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach
AbstractPrevious 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.
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Bibliographic InfoPaper provided by ICER - International Centre for Economic Research in its series ICER Working Papers - Applied Mathematics Series with number 06-2009.
Length: 15 pages
Date of creation: May 2009
Date of revision:
ARFIMA; FIGARCH; long memory; structural change; inflation; G7.;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-05-23 (All new papers)
- NEP-CBA-2009-05-23 (Central Banking)
- NEP-ECM-2009-05-23 (Econometrics)
- NEP-ETS-2009-05-23 (Econometric Time Series)
- NEP-MAC-2009-05-23 (Macroeconomics)
- NEP-MON-2009-05-23 (Monetary Economics)
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