Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks

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.

Number of Pages in PDF File: 29

Keywords: Time varying parameters, Importance sampling, Monte Carlo simulation, Stochastic Volatility, Fractional Integration

JEL Classification: C15, C32, C51, E23, E31

working papers series