A factor-augmented probit model for business cycle analysis

Christophe Bellégo and Laurent Ferrara

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Abstract: Dimension reduction of large data sets has been recently the topic of interest of many research papers dealing with macroeconomic modelling. Especially dynamic factor models have been proved to be useful for GDP nowcasting or short-term forecasting. In this paper, we put forward an innovative factor-augmented probit model in order to analyze the business cycle. Factor estimation is carried either by standard statistical methods or by allowing a richer dynamic behaviour. An application is provided on euro area data in order to point out the ability of the model to detect recessions over the period 1974-2008.

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Date: 2010
Note: View the original document on HAL open archive server: https://hal.science/hal-04140915
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Working Paper: A factor-augmented probit model for business cycle analysis (2010)
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