Econometrics

Statistical methods for economic data, causal inference, and forecasting.

econ.EM · 119 papers

Sequential Estimation of Dynamic Discrete Choice Models with Unobserved Heterogeneity

This paper introduces EM-NPL(q), an efficient framework for estimating dynamic discrete choice models with unobserved heterogeneity, significantly reducing computation time.

2604.26205Apr 29, 2026Ertian Chen, Hiroyuki Kasahara, Katsumi Shimotsu

Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making

BCCB is an online framework for budget-constrained ad allocation, learning user responses and pacing spending, outperforming offline methods in cold-start.

2604.26169Apr 28, 2026Abhirami Pillai

Stochastic Frontier meets Breakdown Frontier

This paper introduces a sensitivity analysis for Stochastic Frontier Models, deriving the breakdown frontier for average inefficiency under relaxed assumptions.

2604.26088Apr 28, 2026Santiago Acerenza, Francisco Rosas

Auditing Marketing Budget Allocation with Hindsight Regret

This paper introduces a retrospective auditing framework using "hindsight regret" to assess marketing budget allocations, providing post-hoc diagnostics.

2604.25977Apr 28, 2026Nilavra Pathak, Olivier Jeunen, Eric Lambert

Identification and Estimation of Consumers' Preferences from Repeated Observations under Nonlinear Pricing

This paper introduces a nonparametric method to identify and estimate consumer preferences and heterogeneity under nonlinear pricing schedules.

2604.25507Apr 28, 2026Samuele Centorrino, Frédérique Fève, Jean-Pierre Florens

Inference for Linear Systems with Unknown Coefficients

This paper introduces novel sample-splitting tests for linear systems with unknown coefficients and non-negativity constraints, valid under weak conditions.

2604.24904Apr 27, 2026Yuehao Bai, Kirill Ponomarev, Andres Santos +3

Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting

Energy-Arena is a dynamic platform for operational energy forecasting, providing a continuously updated benchmark to improve research comparability.

2604.24705Apr 27, 2026Max Kleinebrahm, Jonathan Berrisch, Philipp Eiser +11

Benefits and Costs of Adaptive Sampling

This paper shows adaptive sampling improves estimation precision over uniform designs and proposes policies balancing inference with online experimentation costs.

2604.24652Apr 27, 2026Yu-Shiou Willy Lin, Dae Woong Ham, Iavor Bojinov

Linear estimations of dynamic fixed effects logit models only with time effects

This paper introduces linear estimation methods for dynamic fixed effects logit models with time effects, achieving root-N consistent estimations.

2604.24150Apr 27, 2026Yoshitsugu Kitazawa

Difference-in-differences with a mediator

This paper introduces a difference-in-differences framework to identify natural indirect, direct, and total causal effects in observational studies.

2604.24049Apr 27, 2026Yuhao Deng, Haoyu Wei, Zhongzhe Ouyang

Bootstrapping with AI/ML-generated labels

This paper introduces a coupled-label bootstrap method to correct OLS bias and validate inference when using AI/ML-generated labels with misclassification errors.

2604.23770Apr 26, 2026Timothy Christensen, Silvia Goncalves, Benoit Perron

Estimation of MIDAS Regressions with Errors-in-the-Variables

This paper proposes a consistent estimator for Mixed Data Sampling (MIDAS) regressions when variables have measurement errors, addressing inconsistency issues.

2604.23469Apr 25, 2026Sukhbir Kaur, Sukhbir Singh, Kanchan Jain +1

Misspecification-Averse Estimation

This paper introduces a new constrained multiplier criterion for optimal estimation under likelihood misspecification, proving its asymptotic optimality.

2604.23176Apr 25, 2026Isaiah Andrews, Ricky Li, Yucheng Shang

Realized Regularized Regressions

This paper introduces a continuous-time penalized regression framework for high-frequency data, achieving oracle properties in high dimensions.

2604.23023Apr 24, 2026Aleksey Kolokolov, Shifan Yu

Stacked Triple Differences

Stacked Triple Differences (DDD) resolves issues in conventional DDD under staggered adoption, offering a transparent, regression-based approach for causal inference.

2604.22982Apr 24, 2026Meng Hsuan Hsieh

Causal Identification under Interference: The Role of Treatment Assignment Independence

This paper shows that standard causal identification methods can still identify average direct effects even with interference, given treatment assignment independence.

2604.22532Apr 24, 2026Julius Owusu, Monika Avila Márquez

Inference in Tightly Identified and Large-Scale Sign-Restricted SVARs

This paper introduces a novel reparameterization and HMC method for efficient inference in large-scale, sign-restricted structural VARs.

2604.22445Apr 24, 2026Markku Lanne, Jani Luoto, Adam Rybarczyk

Algorithmic Feature Highlighting for Human-AI Decision-Making

This paper explores algorithmic feature highlighting for human-AI decision-making, addressing computational tractability and human interpretation.

2604.22236Apr 24, 2026Yifan Guo, Jann Spiess

Agentic Artificial Intelligence in Finance: A Comprehensive Survey

This survey explores agentic AI in finance, detailing its architecture, applications, and implications for market efficiency and systemic risk.

2604.21672Apr 23, 2026Irene Aldridge, Jolie An, Riley Burke +23

Nonparametric Point Identification of Treatment Effect Distributions via Rank Stickiness

This paper introduces rank stickiness for nonparametric point identification of treatment effect distributions, allowing rank violations and yielding tighter variance estimates.

2604.21548Apr 23, 2026Tengyuan Liang

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