Econometrics
Statistical methods for economic data, causal inference, and forecasting.
econ.EM · 119 papersQuantifying the Risk-Return Tradeoff in Forecasting
This paper introduces a framework to quantify forecast reliability using risk-adjusted financial performance measures, revealing professional forecasters' robust performance.
Nowcasting Italian Municipal Income with Nightlights: A Deep Learning Approach
A deep learning model using NASA nightlight data accurately nowcasts Italian municipal income, outperforming traditional and spatial econometric benchmarks.
Nonparametric Empirical Bayes Confidence Intervals
This paper introduces Nonparametric Empirical Bayes Confidence Intervals (NP-EBCIs) for individual effects, offering improved inference with asymptotic exactness.
Rolling-Origin Conformal Prediction under Local Stationarity and Weak Dependence
Rolling-origin conformal prediction adapts to time-series dependence, achieving optimal coverage with adaptive calibration windows.
Vibe Econometrics and the Analysis Contract
This paper introduces "vibe econometrics," highlighting how AI-assisted causal analysis democratizes inferential failures and proposes the Analysis Contract to mitigate them.
Self-normalized tests for multistep conditional predictive ability
This paper introduces self-normalized tests for multistep conditional predictive ability, avoiding complex covariance matrix estimation.
Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
Causal EpiNets use an anchored neural architecture and Epistemic Neural Networks to provide precision-corrected, valid bounds on individual treatment effects.
Inference on Linear Regressions with Two-Way Unobserved Heterogeneity
This paper introduces a new method for robust inference in linear panel data models with two-way unobserved heterogeneity.
Scaling the Queue: Reinforcement Learning for Equitable Call Classification Capacity in NYC Municipal Complaint Systems
This paper introduces an equity-centered reinforcement learning framework to optimize complaint classification and routing in NYC's 311 system, addressing service disparities.
Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
This paper argues that debiased machine learning should use regressor balancing guided by the Neyman orthogonal score, not just covariate balancing.
Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand
This paper introduces a new algorithm for contextual dynamic pricing with non-Lipschitz demand, achieving optimal \tilde O(T^{2/3}) regret.
Estimator Averaging of Local Projection and VAR Impulse Responses
A new estimator averaging approach combines Local Projections (LP) and Vector Autoregressions (VAR) for impulse response analysis, minimizing MSE to improve accuracy.
Causal State-Dependent Local Projections
This paper clarifies the causal interpretation of state-dependent local projections (LPs) and introduces a nonparametric estimator for robust causal inference.
MSE-Optimal Difference-in-Differences Estimator
This paper introduces an MSE-optimal Difference-in-Differences estimator that selects pre-trend length to balance bias and variance, improving accuracy.
Approximate Operator Inversion for Average Effects in Nonlinear Panel Models
Introduces Approximate Operator Inversion (AOI) for estimating average effects in nonlinear panel models, offering exponential bias reduction for moderate T.
Efficient GMM and Weighting Matrix under Misspecification
This paper introduces a new misspecification-efficient GMM estimator that outperforms standard GMM under moment condition misspecification.
It's complicated: A Non-parametric Test of Preference Stability between Singles and Couples
This paper develops a non-parametric method to test preference stability between singles and couples, finding that preferences are not stable.
Scalable Structural Estimation of Networked Infrastructure: Exact Decomposition for Localized Coordination
This paper introduces an exact decomposition method for scalable structural estimation in large networked systems with localized interactions.
Uncertainty Quantification in Forecast Comparisons
This paper introduces simultaneous confidence bands to rigorously quantify uncertainty in multi-dimensional forecast comparisons, addressing the multiple comparison problem.
Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators
This paper shows how to correctly estimate variance for modern DiD estimators used with complex survey data, preventing misleading results.
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