Comparative computer simulation and empirical analysis of MIDAS and artificial neural network-UMIDAS models for short- and long-term US GDP forecasting

IF 2.9 Q2 BUSINESS Competitiveness Review Pub Date : 2024-08-27 DOI:10.1108/cr-09-2023-0238
Samir K H. Safi, Olajide Idris Sanusi, Afreen Arif
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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.

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用于短期和长期美国国内生产总值预测的 MIDAS 模型和人工神经网络-UMIDAS 模型的计算机模拟和实证分析比较
目的 本研究旨在评估线性混合数据采样(MIDAS)、非线性人工神经网络(ANN)和一种混合方法,以利用高频信息改善低频国内生产总值(GDP)预测。通过直接预测比较评估了它们的能力。本研究比较了使用五个月度预测因子的无限制 MIDAS(UMIDAS)、独立 ANN 和 ANN 增强 MIDAS 模型的季度 GDP 预测。对最近的美国数据进行了严格的实证分析,并辅以蒙特卡罗模拟来验证研究结果。研究结果实证结果和模拟表明,混合 ANN-MIDAS 在短期预测方面表现最佳,而 UMIDAS 在长期预测方面更为稳健。将 ANNs 集成到 MIDAS 中为近期预测提供了建模灵活性和准确性。扩大变量范围和采用其他数据处理技术可能会揭示更多信息。研究结果指导研究人员和政策制定者在数据复杂的情况下利用混合频率。社会意义加强 GDP 预测有助于改进政策和商业决策,从而提高经济绩效和社会福利。更准确的预测能增强利益相关者对关键选择所依据的统计数据的信心和信任。原创性/价值这项直接预测比较提供了利用领先的统计和机器学习技术混合频率的独特大规模模拟证据。结果阐明了它们在短期与长期建模中的互补性。
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来源期刊
CiteScore
6.60
自引率
17.20%
发文量
50
期刊介绍: The following list indicates the key issues in the Competitiveness Review. We invite papers on these and related topics. Special issues of the Review will collect papers on specific topics selected by the editors. Definition/conceptual framework of competitiveness Competitiveness diagnostics and rankings Competitiveness and economic outcomes Specific dimensions of competitiveness Competitiveness and endowments Competitiveness and economic development Location and business strategy International business and the role of MNCs Innovation and innovative capacity Clusters and cluster initiatives Institutions for competitiveness Public policy (e.g., innovation, cluster development, regional development) The Competitiveness Review aims to publish high quality papers directed at scholars, government institutions, businesses and practitioners. It appears in collaboration with key academic and professional groups in the field of competitiveness analysis and policy, including the Microeconomics of Competitiveness (MOC) network and The Competitiveness Institute (TCI) practitioner network for competitiveness, clusters and innovation.
期刊最新文献
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