Trend and Cycle Shocks in Bayesian Unobserved Components Models for UK Productivity

Seasonality with Trend and Cycle Interactions in Unobserved Components Models

Total Factor Productivity: An Unobserved Components Approach

This work examines the presence of unobserved components in the time series of Total Factor Productivity, which is an idea central to modern Macroeconomics. The main approaches in both the study of economic growth and the study of business cycles rely on certain properties of the different components of the time series of Total Factor Productivity. In the study of economic growth, the Neoclassi...

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 ...

How Do Agricultural Subsectors Respond to Productivity Shocks? Evidence from a Bayesian DSGE Model in Iran

Understanding the dynamics of productivity shocks is instrumental if we are to identify the sources of economic growth. This paper, investigates dynamic effects of positives productivity shocks to agricultural subsectors during the period from 1991-2015, by disaggregating agricultural sector in Iran into four key subsectors (crops, livestock, fishing and forestry) through an estimated DSGE mode...

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 ...