Martingale unobserved component models

Working paper

I discuss models which allow the local level model, which rationalised exponentially weighted moving averages, to have a time-varying signal/noise ratio. I call this a martingale component model. This makes the rate of discounting of data local. I show how to handle such models effectively using an auxiliary particle filter which deploys M Kalman filters run in parallel competing against one another. Here one thinks of M as being 1,000 or more. The model is applied to inflation forecasting. The model generalises to unobserved component models where Gaussian shocks are replaced by martingale difference sequences.

Authors: Shephard, N

• Publication status: Published
• Publisher: University of Oxford
• Series: Department of Economics Discussion Paper Series
• Publication date: 2013-02-10
• Paper number: 644
• Keywords: particle filter; likelihood; forecasting; Kalman filter; EM algorithm; stochastic volatility; auxiliary particle filter; martingale unobserved component model; EWMA