Adaptive ARFIMA models with applications to inflation

• Published in: Economic modelling Vol. 29; no. 6; pp. 2451 - 2459
• Main Authors: Baillie, Richard T., Morana, Claudio
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2012; Elsevier Science Ltd
• Subjects: ARFIMA; Consumer Price Index; Economic models; Evidence; FIGARCH; G-7; Industrialized nations; Inflation; Long memory; Parameter estimation; Structural change; Studies; G7; C22; Inflation; C15; ARFIMA; FIGARCH; Long memory; Structural change
• ISSN: 0264-9993; 1873-6122
• DOI: 10.1016/j.econmod.2012.07.011

Many previous analyses of inflation have used either long memory or nonlinear time series models. This paper suggests a simple adaptive modification of the basic ARFIMA model, which uses a flexible Fourier form to allow for a time varying intercept. Simulation evidence suggests that the model provides a good representation of various forms of structural breaks and also that the new model can be efficiently estimated by a QMLE approach. We investigate monthly CPI inflation series for the G7 countries and find evidence of stable long memory parameters across regimes and also of significant nonlinear effects. The estimated adaptive ARFIMA models generally have less persistent long memory parameters than previous studies, with the estimated time dependent intercept being an important component. The model is also supplemented with an adaptive FIGARCH component, yielding a double nonlinear long memory model. ► This paper suggests a simple adaptive modification of the basic ARFIMA model. ► The model allows for both long memory and structural change. ► Simulation evidence suggests the model can be accurately estimated by QMLE. ► We investigate monthly CPI inflation series for the G7 countries. ► We find stable persistence across regimes and significant nonlinear effects.