Bayesian analysis of dynamic factor models

Project leader

Funding source

Riksbankens Jubileumsfond (RJ)

Project Details

Start date: 01/01/2010
Funding: 2600000 SEK


When forecasting macroeconomic variables, such as GDP-growth and inflation, a huge number of potentially informative time-series of varying quality are often available. Ignoring part of this information will necessarily lead to sub-optimal forecasts. On the other hand, utilizing all series in a statistically and computationally efficient manner is a formidable task.

Within this project, so-called dynamic factor models are studied. Such models have become increasingly popular in finance and economics lately, and aim at summarizing the information content of a large number of variables in a few factors. In contrast to traditionally used factor analysis, dynamic factor models incorporate the serial dependence found in time-series data.

The project will aim at constructing statistically and computationally efficient forecasting methods as well as methods for model specification, with a particular focus on the number of factors to be used as well as detailed modeling of the time-series dynamics

External Partners

Last updated on 2017-23-03 at 09:04