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A factor-augmented probit model for business cycle analysis

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  • Christophe Bellégo
  • Laurent Ferrara

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

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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  • Christophe Bellégo & Laurent Ferrara, 2010. "A factor-augmented probit model for business cycle analysis," Working Papers hal-04140915, HAL.
    • Handle: RePEc:hal:wpaper:hal-04140915
    Note: View the original document on HAL open archive server: https://hal.science/hal-04140915
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    References listed on IDEAS

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