Abstract
Unemployment rate forecasting has become a particularly promising domain of comparative studies in recent years because it is a major issue facing the economic forecasting process. Since the time-series data are rarely pure linear or nonlinear, obviously, sometimes contain both components jointly. Therefore, this study introduces a hybrid model that combines two commonly used models, namely, the Linear Autoregressive Moving Average with exogenous variable (ARMAX) model and nonlinear Generalized Autoregressive Conditional Heteroskedasticity with exogenous variable (GARCHX) model whose conditional variance follows a General error distribution (GED). That is, build a hybrid (ARMAX-GARCHX-GED) model employed in modeling bivariate time-series data of the unemployment rate and exchange rate. Usually, the forecasting performance evaluation based on the common classical forecast accuracy criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percent Error (MAPE) have some specific limitations in application to choosing the optimal forecasting model. Therefore, in this paper, we employed a modern evaluation criterion based on the methodology advocated by Diebold–Mariano (DM) known as (DM test) as a new criterion for evaluation based on statistical hypothesis tests. This (DM test) has been applied in this study to distinguish the significant differences in forecasting accuracy between hybrid (ARMAX-GARCHX-GED) and individual ARMAX models. From the case study results and according to DM-test it is observed that the differences between the forecasting performances of models are significant and the hybrid model (ARMAX-GARCHX-GED) is more efficient than the individual competitive ARMAX model for the unemployment rate forecasting.
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Notes
- 1.
ARMAX model orders were selected based on minimum AIC and BIC criteria and observing the significance of autocorrelation (ACF), partial autocorrelation (PACF), extended autocorrelation function (EACF) and cross-correlation (CCF) functions to identify the model. From Cross-correlation functions (CCF), it is found that the delay time equals to zero. Results implemented using MATLAB (2018a).
- 2.
Results are implemented using EViews 9.
- 3.
Results of Table 4 and graph are implemented using MATLAB (2018a).
- 4.
Results implemented using R 3.5.2.
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The authors would like to thank ITISE 2019 Conference organizers to receive this contribution. The authors would like to thank the editors and all the reviewers for their advice and suggestions on improving this paper.
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Mohammed, F.A., Mousa, M.A. (2020). Applying Diebold–Mariano Test for Performance Evaluation Between Individual and Hybrid Time-Series Models for Modeling Bivariate Time-Series Data and Forecasting the Unemployment Rate in the USA. In: Valenzuela, O., Rojas, F., Herrera, L.J., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis. ITISE 2019. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-56219-9_29
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