Comparative computer simulation and empirical analysis of MIDAS and artificial neural network-UMIDAS models for short- and long-term US GDP forecasting
Abstract
Purpose
This study aims to evaluate linear mixed data sampling (MIDAS), nonlinear artificial neural networks (ANNs) and a hybrid approach for exploiting high-frequency information to improve low-frequency gross domestic product (GDP) forecasting. Their capabilities are assessed through direct forecasting comparisons.
Design/methodology/approach
This study compares quarterly GDP forecasts from unrestricted MIDAS (UMIDAS), standalone ANN and ANN-enhanced MIDAS models using five monthly predictors. Rigorous empirical analysis of recent US data is supplemented by Monte Carlo simulations to validate findings.
Findings
The empirical results and simulations demonstrate that the hybrid ANN-MIDAS performs best for short-term predictions, whereas UMIDAS is more robust for long-term forecasts. The integration of ANNs into MIDAS provides modeling flexibility and accuracy gains for near-term forecasts.
Research limitations/implications
The model comparisons are limited to five selected monthly indicators. Expanding the variables and alternative data processing techniques may reveal further insights. Longer analysis horizons could identify structural breaks in relationships.
Practical implications
The findings guide researchers and policymakers in leveraging mixed frequencies amidst data complexity. Appropriate modeling choices based on context and forecast horizon can maximize accuracy.
Social implications
Enhanced GDP forecasting supports improved policy and business decisions, benefiting economic performance and societal welfare. More accurate predictions build stakeholder confidence and trust in statistics underlying critical choices.
Originality/value
This direct forecasting comparison offers unique large-scale simulation evidence on harnessing mixed frequencies with leading statistical and machine learning techniques. The results elucidate their complementarity for short-term versus long-term modeling.
Keywords
Acknowledgements
The authors gratefully acknowledge funding support from the United Arab Emirates University, which made this research possible.
Funding: The authors declare that this research is made possible through the generous funding received from the Start-up Research Grant (No. 12B025) from the College of Business and Economics at the United Arab Emirates University.
Competing interests: The authors have no relevant financial or non-financial interests to disclose.
Data availability statement: The data supporting the findings of this study are available upon request. Interested researchers may contact the corresponding author to obtain access to the data for further analysis and validation.
Citation
Safi, S.K.H., Sanusi, O.I. and Arif, A. (2024), "Comparative computer simulation and empirical analysis of MIDAS and artificial neural network-UMIDAS models for short- and long-term US GDP forecasting", Competitiveness Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/CR-09-2023-0238
Publisher
:Emerald Publishing Limited
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