455 papers
This category explores the fascinating intersection of economics and emotion, investigating how our feelings shape financial decisions, market trends, and policy outcomes. By bridging behavioral science with traditional economic theory, these studies reveal why people often act against their own best interests when stress, fear, or excitement take the wheel.
Every new preprint in this field arrives directly from arXiv, where researchers share their latest findings before formal publication. At Gist.Science, we process each of these papers immediately, offering both detailed technical breakdowns for experts and clear, plain-language summaries so anyone can grasp the core insights. Below are the latest papers in this emerging area of study, waiting to be explored.
This paper proposes a Conditional Minimax Regret (CMR) rule for two-wave experiments that optimally balances the robustness of balanced assignment with the efficiency of Neyman allocation by minimizing worst-case regret over a finite-sample confidence set, thereby avoiding the severe precision losses of adaptive methods in small pilots while capturing their gains in large ones.
This paper introduces a synthetic control estimator for compositional outcomes based on a random utility model and Aitchison geometry, which is applied to Pennsylvania's electricity mix to reveal a significant, persistent shift toward natural gas and away from renewables following the Alternative Energy Portfolio Standard.
This paper proposes a unified framework for designing cost-constrained experiments that combine with potentially biased observational data by employing a minimax proportional-regret criterion to optimize the bias-variance trade-off without requiring pre-specified bias bounds.
The paper introduces the **mnorm** R package, a tool designed to calculate and differentiate conditional multivariate normal densities and probabilities—including support for Gaussian copulas, parallel computing, and fast bivariate/trivariate routines—to facilitate maximum-likelihood estimation in econometric models.
This paper proposes and evaluates fast Bayesian Model Averaging and Selection procedures for stochastic frontier analysis under non-Gaussian errors, demonstrating through simulations that accounting for asymmetric disturbances significantly impacts posterior inference and model selection outcomes compared to conventional Gaussian-error approaches.
This review bridges the gap between classical econometric Vector Autoregressions and modern AI-based time-series forecasting methods—specifically transformers, large pretrained models, and diffusion-based generative forecasters—by analyzing how they address challenges of high dimensionality, nonstationarity, and nonlinearity while highlighting the trade-off between their flexible predictive power and the loss of structural interpretability essential for policy analysis.
This paper investigates the systematic biases inherent in standard A/B testing designs for multi-item, multi-period inventory systems due to temporal carryover and cannibalization, proposing and validating a pairwise experimental design that effectively mitigates these interference effects.
This paper introduces a novel manipulation testing framework for Regression Discontinuity Designs that leverages Benford's Law to detect structural imbalances in running variables by analyzing directional density components, offering a parameter-free, more sensitive alternative to traditional McCrary-type tests.
This paper proposes a novel Factor-Augmented proxy-SVAR methodology for panel data with strong cross-sectional dependence and limited instruments, which identifies regional policy shocks using global and local instruments to estimate government spending multipliers in Italian regions.
This study proposes a diagnostic framework using complexity and statistical-structure measures on high-frequency trade data to detect artificial transaction patterns, revealing a significant anomaly on the Bitget exchange for BTC and ETH in mid-2025 characterized by a surge in low-volume trades that suggests potential market manipulation.