Moving Average Stochastic Volatility Models with Application to Inflation Forecast
CAMA Working Paper 31/2013
27 Pages Posted: 8 Jun 2013
Date Written: May 2013
Abstract
We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional mean has a state space representation. Having a moving average component, however, means that the errors in the measurement equation are no longer serially independent, and estimation becomes more difficult. We develop a posterior simulator that builds upon recent advances in precision-based algorithms for estimating these new models. In an empirical application involving U.S. inflation we find that these moving average stochastic volatility models provide better in sample fitness and out-of-sample forecast performance than the standard variants with only stochastic volatility.
Keywords: state space, unobserved components model, precision, sparse, density forecast
JEL Classification: C11, C51, C53
Suggested Citation: Suggested Citation