Long memory modelling of inflation with stochastic variance and structural breaks

Charles Bos, Siem Jan Koopman and Marius Ooms

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain efficient estimates of the parameters using a monthly dataset of core inflation for which we consider different subsamples of varying size. Based on the new modelling framework and the associated estimation technique, we find remarkable changes in the variance, in the order of integration, in the short memory characteristics and in the volatility of volatility.

Keywords: Time varying parameters; Importance sampling; Monte Carlo simulation; Stochastic Volatility; Fractional Integration (search for similar items in EconPapers)
JEL-codes: C15 C32 C51 E23 E31 (search for similar items in EconPapers)
Pages: 27
Date: 2007-12-21
New Economics Papers: this item is included in nep-cba, nep-ets, nep-mac and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2007-44

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