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

Emilio Zanetti Chini ()

International Journal of Forecasting, 2018, vol. 34, issue 4, 711-732

Abstract: The generalized smooth transition autoregression (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 autoregressions are nested in the GSTAR. A test for the null hypothesis of dynamic symmetry is presented. Two case studies indicate that dynamic asymmetry is a key feature of the U.S. economy. The GSTAR model beats its competitors for point forecasting, but this superiority becomes less evident for 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)
Date: 2018
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Working Paper: Forecasting dynamically asymmetric fluctuations of the U.S. business cycle (2018) Downloads
Working Paper: Forecasting dynamically asymmetric fluctuations of the U.S. business cycle (2018) Downloads
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