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

Fabio Gobbi

Advances in Management and Applied Economics, 2021, vol. 11, issue 6, 7

Abstract: The aim of the paper is to compare the forecasting performance of a class of state-dependent 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. JEL classification numbers: C22, E37, F47.

Keywords: Nonlinear models for time series; GDP growth rate; Forecasting accuracy. (search for similar items in EconPapers)
Date: 2021
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