Copertina

Forecasting Disaggregated Producer Prices: A Fusion of Machine Learning and Econometric Techniques

Fuente: Wiley Open Access

Salvato in:

Dettagli Bibliografici
Autore principale:	Soňa Benecká
Natura:	Artículo Open Access
Pubblicazione:	Wiley 2026
Soggetti:	Journal of Forecasting
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning
di: Theo Berger, et al.
Pubblicazione: (2024)

Explainable Soybean Futures Price Forecasting Based on Multi‐Source Feature Fusion
di: Binrong Wu, et al.
Pubblicazione: (2024)

Dynamic Econometric Models: A State‐Space Formulation
di: Mariane B. Alves, et al.
Pubblicazione: (2025)

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

Forecasting and Pricing in the Carbon Credits Market
di: Yiyang Chen, et al.
Pubblicazione: (2026)

Forecasting House Prices: The Role of Market Interconnectedness
di: Zac Chen, et al.
Pubblicazione: (2026)

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

Forecasting With Machine Learning Shadow‐Rate VARs
di: Michael Grammatikopoulos
Pubblicazione: (2025)

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

Can Attention Mechanisms Improve Carbon Price Forecasting Accuracy?
di: Ting Yao, et al.
Pubblicazione: (2025)

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

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

Using a Wage–Price‐Setting Model to Forecast US Inflation
di: Nguyen Duc Do
Pubblicazione: (2024)

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

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

Adaptive Model Integration for Stock Price Forecasting With CNN‐ATTN‐BiLSTM
di: Yi Xiao, et al.
Pubblicazione: (2026)

Deciphering Long‐Term Economic Growth: An Exploration With Leading Machine Learning Techniques
di: Zin Mar Oo, et al.
Pubblicazione: (2025)

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

A New Multivariate Decomposition–Ensemble Approach With Multisource Heterogeneous Data for Crude Oil Price Forecasting
di: Zhengling Zhao, et al.
Pubblicazione: (2026)

On Capturing Multi‐Scale Market Dynamics for High‐Frequency Stock Price Forecasting Using a Hybrid Attention‐Based Deep Learning Model
di: Runze Jiang, et al.
Pubblicazione: (2026)

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

Forecasting European Union Electronic Trading Systems Phase 4 Spot Prices Using Data‐Driven Hybrid Deep Learning Models: Integrating Energy and Market Activity as Controls
di: Noman Arshed, et al.
Pubblicazione: (2026)

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

Support Vector Machine to Forecast Reexamination Invalidation Decisions for Utility Model Patent
di: Mei‐Hsin Wang, et al.
Pubblicazione: (2025)

A Novel Approach to Forecasting After Large Forecast Errors
di: Jennifer L. Castle, et al.
Pubblicazione: (2025)

Seasonal Decomposition‐Enhanced Deep Learning Architecture for Probabilistic Forecasting
di: Keyan Jin, et al.
Pubblicazione: (2025)

Fiscal Forecasting Rationality Among Expert Forecasters
di: Belen Chocobar, et al.
Pubblicazione: (2024)

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

Forecasting in turbulent times
di: Nikolaos Giannellis, et al.
Pubblicazione: (2024)

A Hybrid Deep Learning Model for Coal Index Forecasting Based on Sentiment Analysis and Decomposition–Reconstruction Methods
di: Yi Xiao, et al.
Pubblicazione: (2025)

Stock Price Limit and Its Predictability in the Chinese Stock Market
di: Haohui Liang, et al.
Pubblicazione: (2024)

Information Illusion: Different Amounts of Information and Stock Price Estimates
di: Andreas Oehler, et al.
Pubblicazione: (2025)

Macroeconomic and Financial Forecasting in the Aftermath of the Covid Shock: Inflation Forecasting With Large Language Models
di: Nikolaos Giannellis, et al.
Pubblicazione: (2026)

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

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

Coherent Forecasting of Realized Volatility
di: Marius Puke, et al.
Pubblicazione: (2026)

Crossproduct Effect and Volatility Forecasting
di: Jiafu Xu, et al.
Pubblicazione: (2024)

A Comparison of Realized Measures of Integrated Volatility: Price Duration‐ vs. Return‐Based Approaches
di: Björn Schulte‐Tillmann, et al.
Pubblicazione: (2026)

Spread Option Pricing Method Based on Nonparametric Predictive Inference Copula
di: Ting He
Pubblicazione: (2025)