Time-varying persistence of inflation: evidence from a wavelet-based approach

Boubaker Heni, Giorgio Canarella, Rangan Gupta and Stephen Miller
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Boubaker Heni: IPAG LAB, IPAG Business School, Paris 75006, France

Studies in Nonlinear Dynamics & Econometrics, 2017, vol. 21, issue 4, 18

Abstract: We propose a new stochastic long-memory model with a time-varying fractional integration parameter, evolving non-linearly according to a Logistic Smooth Transition Autoregressive (LSTAR) specification. To estimate the time-varying fractional integration parameter, we implement a method based on the wavelet approach, using the instantaneous least squares estimator (ILSE). The empirical results show the relevance of the modeling approach and provide evidence of regime change in inflation persistence that contributes to a better understanding of the inflationary process in the US. Most importantly, these empirical findings remind us that a “one-size-fits-all” monetary policy is unlikely to work in all circumstances. The empirical results are consistent with newly developed tests of wavelet-based unit root and fractional Brownian motion.

Keywords: ILSE estimator; LSTAR model; MODWT algorithm; time-varying long-memory (search for similar items in EconPapers)
JEL-codes: C13 C22 C32 C54 E31 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Time-Varying Persistence of Inflation: Evidence from a Wavelet-Based Approach (2016)
Working Paper: Time-Varying Persistence of Inflation: Evidence from a Wavelet-based Approach (2016)
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DOI: 10.1515/snde-2016-0130

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