Department of Economics and Business Economics

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

Research output: Working paper

Standard

Forecasting dynamically asymmetric fluctuations of the U.S. business cycle. / Chini, Emilio Zanetti.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2018.

Research output: Working paper

Harvard

Chini, EZ 2018 'Forecasting dynamically asymmetric fluctuations of the U.S. business cycle' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Chini, E. Z. (2018). Forecasting dynamically asymmetric fluctuations of the U.S. business cycle. Aarhus: Institut for Økonomi, Aarhus Universitet. CREATES Research Papers, No. 2018-13

CBE

Chini EZ. 2018. Forecasting dynamically asymmetric fluctuations of the U.S. business cycle. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Chini, Emilio Zanetti Forecasting dynamically asymmetric fluctuations of the U.S. business cycle. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2018-13). 2018., 41 p.

Vancouver

Chini EZ. Forecasting dynamically asymmetric fluctuations of the U.S. business cycle. Aarhus: Institut for Økonomi, Aarhus Universitet. 2018 Apr 3.

Author

Chini, Emilio Zanetti. / Forecasting dynamically asymmetric fluctuations of the U.S. business cycle. Aarhus : Institut for Økonomi, Aarhus Universitet, 2018. (CREATES Research Papers; No. 2018-13).

Bibtex

@techreport{2b7318600e6a46a9bac7a0ff4faf8149,
title = "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle",
abstract = "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",
author = "Chini, {Emilio Zanetti}",
year = "2018",
month = "4",
day = "3",
language = "English",
publisher = "Institut for \{O}konomi, Aarhus Universitet",
type = "WorkingPaper",
institution = "Institut for \{O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Forecasting dynamically asymmetric fluctuations of the U.S. business cycle

AU - Chini,Emilio Zanetti

PY - 2018/4/3

Y1 - 2018/4/3

N2 - 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.

AB - 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.

KW - Density forecasts, Econometric modelling, Evaluating forecasts, Generalized logistic, Industrial production, Nonlinear time series, Point forecasts, Statistical tests, Unemployment

M3 - Working paper

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

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -