Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks
Charles S. Bos
VU University Amsterdam
Siem Jan Koopman
VU University Amsterdam; Tinbergen Institute
VU University Amsterdam - Department of Econometrics
December 21, 2007
Tinbergen Institute Discussion Paper No. 2007-099/4
CREATES Research Paper No. 2007-44
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
Date posted: January 11, 2008
© 2015 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo1 in 0.578 seconds