Skill UI

A comprehensive guide for constructing and applying structural models in health economics, particularly for interpreting empirical estimates or running policy counterfactuals. It covers core archetypes—including demand-selection, provider incentives, and human capital—and emphasizes critical rigor, such as parameter identification, adherence to institutional rules (e.g., risk adjustment), and defending policy-invariance (Lucas critique).

A comprehensive guide for establishing rigorous identification strategies in monetary, banking, and macro-financial modeling. It details the standards required for publishing causal claims in top-tier journals (like JMCB), covering three branches: macro shock identification (SVAR), structural parameter identification, and micro-banking causal designs (e.g., staggered regulations). Learn how to defend against information effects, address endogeneity, and ensure policy relevance.

A guide for integrating behavioral theory into rigorous empirical studies. It outlines the necessary steps to move beyond descriptive findings of irrationality by constructing a statistically identifiable behavioral model (e.g., incorporating loss aversion parameters) and testing it against rational baselines using controlled, incentive-compatible experimental designs. Essential for submitting papers requiring model explanation of behavioral biases.

A guide for designing rigorous numerical experiments and simulations for academic manuscripts in management science. This methodology focuses on validating proven theoretical properties, assessing algorithm performance, exploring parameter sensitivity, and extracting actionable managerial insights. It emphasizes that simulations must complement, but never replace, formal mathematical proofs.

A comprehensive guide for strengthening empirical causal claims for top-tier economics journals. This framework teaches how to move beyond simple OLS regressions by rigorously testing for causal identification (e.g., DID, IV, RDD) and, crucially, mapping the statistical estimate back to a meaningful economic parameter or mechanism. It emphasizes the dual requirement of credible identification and deep economic interpretability.

Provides comprehensive guidelines for building and validating complex structural economic models, including CGE, IO, and Structural Decomposition. It emphasizes mandatory elements like explicit data sourcing, parameter calibration, macroeconomic closure rules, and scenario reproducibility to ensure models are not 'black boxes' and are scientifically rigorous.

A detailed guide on establishing and reporting the necessary robustness and sensitivity analyses for manuscripts in quantitative economics and econometrics. It clarifies that the required robustness differs significantly from causal inference studies, emphasizing testing against variations in method choice, parameters, data scope, and sample periods. Provides structured methodologies for generating sensitivity matrices and discussing model stability.

A rigorous methodological framework for identifying core risk and uncertainty parameters in academic research. This guide details how to design experiments or structural models to explicitly separate confounds (e.g., utility curvature vs. probability weighting, preferences vs. beliefs) and ensure that the measured parameter is genuinely identified, thereby enhancing the scientific rigor of behavioral finance and econometrics studies.

A comprehensive framework for conducting and structuring robustness checks in academic papers, particularly in econometrics and behavioral economics. It guides researchers on identifying specific threats to the core parameter (e.g., functional form dependence, incentive bias, sample selection) and organizing tests by threat, ensuring the findings are statistically reliable and withstand expert scrutiny.

This guide provides rigorous standards for transforming complex behavioral and risk data into clear, statistically sound, and academically rigorous tables and figures. It ensures that key decision-theoretic content, such as choice patterns, parameter estimates, and uncertainty, are visibly and interpretably presented for academic publication.

A comprehensive guide for developing rigorous spatial economic models, designed for manuscripts needing deep theoretical interpretation (e.g., JUE submissions). It instructs users on how to build quantitative spatial models (QSMs), sorting models, and spatial-equilibrium frameworks to interpret reduced-form estimates, establish counterfactual policies, and ensure that theory rigorously disciplines the empirical findings. Focus areas include general-equilibrium reallocation, policy invariance, and identifying parameters.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).