Martingale unobserved component models

Abstract

Abstract This chapter generalizes the familiar linear Gaussian unobserved component models or structural time series models to martingale unobserved component models. This generates forecasts whose rate of discounting of data is time-varying or local. How to handle such models is shown, effectively using an auxiliary particle filter which deploys M competing Kalman filters run in parallel. Here, one thinks of M as being 1000 or more. The model is applied to inflation forecasting. The martingale unobserved component model generalizes in several ways, to allow for trends, cycles, and seasonal components, and the methods developed in this chapter can be extended in the same way.

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