Seasonality with trend and cycle interactions in unobserved components models

Seasonality with Trend and Cycle Interactions in 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 ...

Some Reflections on Trend-Cycle Decompositions with Correlated Components

This paper discusses a few interpretative issues arising from trend-cycle decompositions with correlated components. We determine the conditions under which correlated components may originate from: underestimation of the cyclical component; a cycle in growth rates, rather than in the levels; the hysteresis phenomenon; permanenttransitory decompositions, where the permanent component has richer...

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 ...

Modeling the Phillips Curve with Unobserved Components

The relationship between in‡ation and the output gap can be modeled simply and e¤ectively by including an unobserved random walk component in the model. The dynamic properties match the stylized facts and the random walk component satis…es the properties normally required for core in‡ation. The model may be generalized to as to include a term for the expectation of next period’s output, but it ...