Department of Economics and Business Economics

Forecasting dynamically asymmetric fluctuations of the U.S. business cycle

Research output: Working paperResearch


  • rp18_13

    Final published version, 1.7 MB, PDF document

  • Emilio Zanetti Chini, University of Pavia, Italy
The Generalized Smooth Transition Auto-Regression (GSTAR) parametrizes the joint asymmetry in the duration and length of cycles in macroeconomic time series by using particular generalizations of the logistic function. The symmetric smooth transition and linear auto-regressions are peculiar cases of the new parametrization. A test for the null hypothesis of dynamic symmetry is discussed. Two case studies indicate that dynamic asymmetry is a key feature of the U.S. economy. Our model beats its competitors in point forecasting, but this superiority becomes less evident in density forecasting and in uncertain forecasting environments.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages41
Publication statusPublished - 3 Apr 2018
SeriesCREATES Research Papers

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