The aim of the paper is to compare the forecasting performance of a class of statedependent autoregressive (SDAR) models for univariate time series with two alternative families of nonlinear models, such as the SETAR and the GARCH models. The study is conducted on US GDP growth rate using quarterly data. Two methods of forecast comparison are employed. The first method consists in evaluation the average performance by using two measures such as the root mean square error (RMSE) and the mean absolute error (MAE) over different forecast horizons, while the second method make use of one of the most used statistical test to compare the accuracy of two forecast methods such as the Diebold-Mariano test.

Gobbi, F. (2021). Evaluating Forecasts from State-Dependent Autoregressive Models for US GDP Growth Rate. Comparison with Alternative Approaches. ADVANCES IN MANAGEMENT AND APPLIED ECONOMICS, 11(6), 117-138 [10.47260/amae/1167].

Evaluating Forecasts from State-Dependent Autoregressive Models for US GDP Growth Rate. Comparison with Alternative Approaches

Gobbi F.
2021-01-01

Abstract

The aim of the paper is to compare the forecasting performance of a class of statedependent autoregressive (SDAR) models for univariate time series with two alternative families of nonlinear models, such as the SETAR and the GARCH models. The study is conducted on US GDP growth rate using quarterly data. Two methods of forecast comparison are employed. The first method consists in evaluation the average performance by using two measures such as the root mean square error (RMSE) and the mean absolute error (MAE) over different forecast horizons, while the second method make use of one of the most used statistical test to compare the accuracy of two forecast methods such as the Diebold-Mariano test.
2021
Gobbi, F. (2021). Evaluating Forecasts from State-Dependent Autoregressive Models for US GDP Growth Rate. Comparison with Alternative Approaches. ADVANCES IN MANAGEMENT AND APPLIED ECONOMICS, 11(6), 117-138 [10.47260/amae/1167].
File in questo prodotto:
File Dimensione Formato  
Gobbi_Vol 11_6_7.pdf

accesso aperto

Tipologia: PDF editoriale
Licenza: Creative commons
Dimensione 467.6 kB
Formato Adobe PDF
467.6 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1162548