Testing Stationarity for Unobserved Components Models

Forecasting with Unobserved Components Time Series Models

Structural time series models are formulated in terms of components, such as trends, seasonals and cycles, that have a direct interpretation. As well as providing a framework for time series decomposition by signal extraction, they can be used for forecasting and for ‘nowcasting’ . The structural interpretation allows extensions to classes of models that are able to deal with various issues in ...

Signal Extraction and the Formulation of Unobserved Components Models

This paper looks at unobserved components models and examines the implied weighting patterns for signal extraction. There are three main themes. The rst is the implications of correlated disturbances driving the components, especially those cases in which the correlation is perfect. The second is how setting up models with t distributed disturbances leads to weighting patterns which are robust ...

Forecasting economic time series using unobserved components time series models

A preliminary version, please do not quote

Seasonality with Trend and Cycle Interactions in Unobserved Components Models

On the Identification of Multivariate Correlated Unobserved Components Models

This paper analyses identification for multivariate unobserved components models in which the innovations to trend and cycle are correlated. We address order and rank criteria as well as potential non-uniqueness of the reduced-form VARMA model. Identification is shown for lag lengths larger than one in case of a diagonal vector autoregressive cycle. We also discuss UC models with common feature...