Forecasting dynamically asymmetric fluctuations of the U.S. business cycle
Emilio Zanetti Chini ()
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
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.
Keywords: Density forecasts; Econometric modelling; Evaluating forecasts; Generalized logistic; Industrial production; Nonlinear time series; Point forecasts; Statistical tests; Unemployment (search for similar items in EconPapers)
JEL-codes: C22 C51 C52 (search for similar items in EconPapers)
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Journal Article: Forecasting dynamically asymmetric fluctuations of the U.S. business cycle (2018)
Working Paper: Forecasting dynamically asymmetric fluctuations of the U.S. business cycle (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2018-13
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