Imagen de Portada

Machine Learning for Detecting Collusion and Capacity Withholding in Wholesale Electricity Markets

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Proz, Jeremy, Huber, Martin
Formato:	Preprint
Publicado:	2025
Materias:	Econometrics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Collusion Detection with Graph Neural Networks
por: Gomes, Lucas, et al.
Publicado: (2024)

Empirical Validation of a Dual-Defense Mechanism Reshaping Wholesale Electricity Price Dynamics in Singapore
por: Zhenyu, Huang, et al.
Publicado: (2026)

Machine Learning the Macroeconomic Effects of Financial Shocks
por: Hauzenberger, Niko, et al.
Publicado: (2024)

An Introduction to Causal Discovery
por: Huber, Martin
Publicado: (2024)

Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder
por: Huber, Martin, et al.
Publicado: (2026)

When Do Treatment Changes Identify Causal Effects?
por: Huber, Martin
Publicado: (2026)

Modeling European Electricity Market Integration during turbulent times
por: Ravazzolo, Francesco, et al.
Publicado: (2025)

Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies
por: Muny, Fabian
Publicado: (2025)

Modelling and Forecasting Energy Market Volatility Using GARCH and Machine Learning Approach
por: Chung, Seulki
Publicado: (2024)

Learning control variables and instruments for causal analysis in observational data
por: Apfel, Nicolas, et al.
Publicado: (2024)

A joint test of unconfoundedness and common trends
por: Huber, Martin, et al.
Publicado: (2024)

Difference-in-differences for mediation analysis using double machine learning
por: Huber, Martin, et al.
Publicado: (2026)

Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning
por: Haddad, Michel F. C., et al.
Publicado: (2024)

Catching Bid-rigging Cartels with Graph Attention Neural Networks
por: Imhof, David, et al.
Publicado: (2025)

Interacting Treatments with Endogenous Takeup
por: Kormos, Mate, et al.
Publicado: (2023)

Cost Pass‐Through in Commodity Markets With Capacity Constraints and International Linkages
por: Reinhard Ellwanger, et al.
Publicado: (2024)

Unleashing the Power of AI: Transforming Marketing Decision-Making in Heavy Machinery with Machine Learning, Radar Chart Simulation, and Markov Chain Analysis
por: Tian, Tian, et al.
Publicado: (2024)

Testing Effect Homogeneity and Confounding in High-Dimensional Experimental and Observational Studies
por: Armendariz, Ana, et al.
Publicado: (2026)

Testing identification in mediation and dynamic treatment models
por: Huber, Martin, et al.
Publicado: (2024)

An Econometric Analysis of Large Flexible Cryptocurrency-mining Consumers in Electricity Markets
por: Majumder, Subir, et al.
Publicado: (2024)

Comprehensive Causal Machine Learning
por: Lechner, Michael, et al.
Publicado: (2024)

Double Machine Learning for Time Series
por: Ciganovic, Milos, et al.
Publicado: (2026)

Debiased Machine Learning U-statistics
por: Escanciano, Juan Carlos, et al.
Publicado: (2022)

Market Sensitivities and Growth Differentials Across Australian Housing Markets
por: Sijp, Willem P
Publicado: (2025)

Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification
por: Huber, Florian, et al.
Publicado: (2023)

Testing Full Mediation of Treatment Effects and the Identifiability of Causal Mechanisms
por: Huber, Martin, et al.
Publicado: (2026)

Bayesian Double Machine Learning for Causal Inference
por: DiTraglia, Francis J., et al.
Publicado: (2025)

On the Asymptotic Properties of Debiased Machine Learning Estimators
por: Velez, Amilcar
Publicado: (2024)

On the (Mis)Use of Machine Learning with Panel Data
por: Cerqua, Augusto, et al.
Publicado: (2024)

Estimating Duration Dependence in Job Search: the Within-Estimation Duration Bias
por: Zuchuat, Jeremy
Publicado: (2025)

Monthly GDP nowcasting with Machine Learning and Unstructured Data
por: Tenorio, Juan, et al.
Publicado: (2024)

Interacting Treatments With Endogenous Takeup
por: Máté Kormos, et al.
Publicado: (2025)

Temporal Disaggregation of GDP: When Does Machine Learning Help?
por: Jung, Yonggeun
Publicado: (2025)

Nowcasting R&D Expenditures: A Machine Learning Approach
por: Aboutorabi, Atin, et al.
Publicado: (2024)

Machine Learning Debiasing with Conditional Moment Restrictions: An Application to LATE
por: Argañaraz, Facundo, et al.
Publicado: (2024)

Sparse Interval-valued Time Series Modeling with Machine Learning
por: Bao, Haowen, et al.
Publicado: (2024)

Automatic Locally Robust GMM with Machine-Learning-Generated Regressors
por: Escanciano, Juan Carlos, et al.
Publicado: (2023)

Bayesian modelling of VAR precision matrices using stochastic block networks
por: Huber, Florian, et al.
Publicado: (2024)

Peer Effects in Labor Market Training
por: Unterhofer, Ulrike
Publicado: (2022)

Big Data Analytics-Enabled Dynamic Capabilities and Market Performance: Examining the Roles of Marketing Ambidexterity and Competitor Pressure
por: Haider, Gulfam, et al.
Publicado: (2024)