Cover Image

Deciphering Long‐Term Economic Growth: An Exploration With Leading Machine Learning Techniques

Fuente: Wiley Open Access

Saved in:

Bibliographic Details
Main Authors:	Zin Mar Oo, Ching‐Yang Lin, Makoto Kakinaka
Format:	Artículo Open Access
Published:	Wiley 2025
Subjects:	Journal of Forecasting
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Term Spread Volatility as a Leading Indicator of Economic Activity
by: Anastasios Megaritis, et al.
Published: (2026)

Medium‐ to Long‐Term Demand Forecasting in Retail and Manufacturing Organizations: Integration of Machine Learning, Human Judgment, and Interval Variable
by: Sushil Punia
Published: (2025)

Deep Learning and Machine Learning Insights Into the Global Economic Drivers of the Bitcoin Price
by: Nezir Köse, et al.
Published: (2025)

Forecasting Disaggregated Producer Prices: A Fusion of Machine Learning and Econometric Techniques
by: Soňa Benecká
Published: (2026)

Innovative Techniques to Predict Churn in the French Insurance Industry: Integration of Machine Learning With the Grabit Model
by: Christophe Schalck, et al.
Published: (2025)

A Review of Methods for Long‐Term Electric Load Forecasting
by: Thangjam Aditya, et al.
Published: (2024)

A Universal Multivariate Long‐Term Time‐Series Robust Forecasting Model With Distinguishable Variable Identifier
by: Xin‐ji Chen, et al.
Published: (2026)

GDP Nowcasting With Artificial Neural Networks: How Much Does Long‐Term Memory Matter?
by: Kristóf Németh, et al.
Published: (2025)

A Rich‐Spatial and Multiscaled Transformer‐Based Approach for Long‐Term Multivariate Time‐Series Forecasting Problem
by: Linh Nguyen Thi My, et al.
Published: (2025)

Leveraging Structural Insights: Enhancing Inflation Forecasting With Dynamic Stochastic General Equilibrium‐Informed Long Short‐Term Memory Models
by: Langfeng Zhou, et al.
Published: (2026)

Forecasting With Machine Learning Shadow‐Rate VARs
by: Michael Grammatikopoulos
Published: (2025)

Fire Prediction and Risk Identification With Interpretable Machine Learning
by: Shan Dai, et al.
Published: (2025)

Using DSGE and Machine Learning to Forecast Public Debt for France
by: Emmanouil Sofianos, et al.
Published: (2026)

Forecasting the Volatility of US Oil and Gas Firms With Machine Learning
by: Juan D. Díaz, et al.
Published: (2024)

Combining Sampling Methods, Cost‐Sensitive Learning, and Ensemble Techniques for Highly Class‐Imbalanced Financial Distress Prediction
by: Wei‐Qiang Huang, et al.
Published: (2026)

Trade and Economic Activity: Nonlinear Modeling and Forecasting
by: Alessandro Borin, et al.
Published: (2024)

Random Integrated Subdata Ensemble Method for Key Variable Selection in Rare Event Setting
by: Ching‐Chi Yang, et al.
Published: (2026)

Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
by: Talha Omer, et al.
Published: (2026)

Machine Learning Forecasts of Tail‐Risk Spillovers in Carbon and Energy Markets
by: Shengnan Liu, et al.
Published: (2026)

Nowcasting World Trade With Machine Learning: A Three‐Step Approach
by: Menzie Chinn, et al.
Published: (2026)

Novel Aligned Correlation Method to Estimate Lead–Lag Relationship Between Time Series
by: Kartikay Gupta, et al.
Published: (2025)

The Impact of News Sentiment on the Bitcoin Price via Machine Learning and Deep Learning‐Based NLP Models
by: Yunus Emre Gür, et al.
Published: (2025)

Forecasting Corporate Default Risk Across Multiple Horizons With Interpretable Machine Learning
by: Qingli Dong, et al.
Published: (2026)

Lost in Translation? Risk‐Adjusting RMSE for Economic Forecast Performance
by: Lukas Salcher, et al.
Published: (2026)

Predicting Equity Premium: A New Momentum Indicator Selection Strategy With Machine Learning
by: Yong Qu, et al.
Published: (2024)

A Frailty Cumulative Link Model for Enhanced Prediction of Loss Given Default Distribution
by: Ruey‐Ching Hwang, et al.
Published: (2025)

Nonstationary Functional Time Series Forecasting
by: Han Lin Shang, et al.
Published: (2024)

Global Risk Aversion: Driving Force of Future Real Economic Activity
by: Jinhwan Kim, et al.
Published: (2024)

Component‐Driven FX Volatility Prediction: Evidence From USDCNH via GARCH‐MIDAS Models Exploiting Leading Indicators
by: Denis Haoheng Wu, et al.
Published: (2025)

When Are Statistical Forecast Gains Economically Relevant? Evidence From Bitcoin Returns
by: Rehan Arain, et al.
Published: (2025)

Global Insights Into Term Spreads: Unveiling Their Predictive Power During Unconventional Monetary Policy
by: Petri Kuosmanen, et al.
Published: (2026)

A Multiscale Transformer Model for Long Time Series Forecasting Based on Discrete Wavelet Transform and Residual Learning Modules
by: Menghan Li, et al.
Published: (2025)

Bank Capital Requirements, Lending Supply, and Economic Activity: A Scenario Analysis Perspective
by: Antonio M. Conti, et al.
Published: (2025)

Correction to “Smart Forecasting of Carbon Prices Using Machine Learning and Neural Networks: When ARIMA Meets XGBoost and LSTM”
Published: (2026)

Leveraging Machine Learning to Predict Food Waste Quantity: Focusing on Military Dining Facilities as Large‐Scale Food Service Operations
by: YongSun Kim, et al.
Published: (2026)

Enhancing the Potential of Random Forest Technique With an Adaptive Horse Herd Optimization Framework for Electric Load Prediction
by: Manvi Mishra, et al.
Published: (2026)

Constructing a high‐frequency World Economic Gauge using a mixed‐frequency dynamic factor model
by: Chew Lian Chua, et al.
Published: (2024)

Forecasting Natural Gas Futures Prices Using Hybrid Machine Learning Models During Turbulent Market Conditions: The Case of the Russian–Ukraine Crisis
by: Pavan Kumar Nagula, et al.
Published: (2025)

Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH‐MIDAS Model
by: Afees A. S alisu, et al.
Published: (2025)

Image‐Based Deep Learning Models for Stock Predictions: Combining Line, Candlestick, and Bar Charts
by: Wei‐Chao Lin, et al.
Published: (2026)