Stochastic Volatility Models with ARMA Innovations: An Application to G7 Inflation Forecasts

We introduce a new class of stochastic volatility models with autoregressive moving
average (ARMA) innovations. The conditional mean process has a flexible form that can
accommodate both a state space representation and a conventional dynamic
regression. The ARMA component introduces serial dependence which renders
standard Kalman filter techniques not directly applicable. To overcome this hurdle we
develop an efficient posterior simulator that builds on recently developed precisionbased
algorithms. We assess the usefulness of these new models in an inflation
forecasting exercise across all G7 economies. We find that the new models generally
provide competitive point and density forecasts compared to standard benchmarks, and
are especially useful for Canada, France, Italy and the US.