Skill UI

Provides rigorous guidelines for analyzing experimental game data and constructing numerical examples for high-level theoretical journals. Emphasizes that analysis must be tightly linked to theoretical claims, such as matching observed outcomes to predicted equilibria, rather than serving as general causal evidence. Covers session-level clustering, structural fitting, and ensuring computational reproducibility.

This guide outlines the best practices and norms for executing and reporting empirical analyses in governance and comparative political science. It covers essential methods including cross-national inference, clustering uncertainty, robustness checks, multi-method triangulation, and ensuring reproducibility, guiding researchers to conduct rigorous, publication-ready studies.

A comprehensive framework for conducting rigorous empirical data analysis required for top-tier academic journals. It guides users through building transparent archival samples, specifying appropriate panel data estimators (like firm/year fixed effects), ensuring correct standard error clustering, and executing sophisticated identification strategies (e.g., Difference-in-Differences, Instrumental Variables). Essential for addressing endogeneity and passing deep reviewer scrutiny.

A comprehensive guide for econometricians submitting manuscripts to top journals. It details how to structure analyses for full reproducibility, including using master scripts, pinning software versions, and exporting results to plain-text formats. Coverage includes advanced robust inference techniques such as HAC, cluster bootstrapping, and weak-IV diagnostics, ensuring the work satisfies rigorous archival standards.

A comprehensive guide for executing and reporting the full empirical robustness battery required by top accounting journals. It covers modern econometric techniques like standard error clustering, advanced Difference-in-Differences (DiD) estimators for staggered timing, Regression Discontinuity (RD) diagnostics, and Instrumental Variables (IV/2SLS). The guide emphasizes rigorous validation of accounting constructs, executing multiple sensitivity tests, and ensuring full data-and-code reproducibility for academic publication.

A comprehensive guide for stress-testing and defending empirical identification strategies in finance and economics manuscripts. It covers advanced econometric techniques such as Staggered DiD, Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Event Studies, providing actionable advice on fixed effects, clustering, necessary robustness checks, and anticipating referee challenges.

A comprehensive guide for conducting advanced empirical analysis required for top-tier finance journals. It covers assembling multi-source firm panels (Compustat, CRSP, etc.), constructing complex corporate finance variables, implementing advanced econometric techniques (high-dimensional fixed effects, clustered errors), and systematically planning robustness checks and replication strategies.

A comprehensive guide detailing the conventions for constructing high-quality empirical tables and figures, especially for corporate finance and econometrics journals. Covers best practices for reporting summary statistics, fixed effects, clustering, event-study/CAR plots, and ensuring self-contained notes for academic publication.

A comprehensive guide for conducting rigorous empirical analysis on field and survey data in low- and middle-income settings. It covers advanced methods for handling clustered sampling, measurement error, differential attrition, and treatment-effect heterogeneity, ensuring results meet the high standards of reproducibility and robustness required by leading developmental economics journals.

This skill assists researchers in creating methodologically rigorous study designs for educational psychology manuscripts, especially when dealing with nested data structures (e.g., students within classes within schools). It guides users in matching the unit of randomization, power, and analysis to the correct cluster level. Focus areas include ensuring robust cluster-level power, establishing ecological validity, developing pre-analysis plans, and adhering to preregistration standards.

A comprehensive methodological guide for conducting rigorous quantitative research in finance, covering asset pricing, corporate finance, and econometrics. This skill defines the necessary standards for factor construction, portfolio sorting, advanced estimators (Fama-MacBeth, GMM), robust standard error clustering, and multiple-testing discipline. It ensures research designs are academically sound, reproducible, and suitable for top-tier financial journals.

A comprehensive guide for planning and stress-testing empirical analysis in financial intermediation, specifically for high-impact journals. It covers the construction of bank/loan-level panel datasets, defining variables (growth vs. levels), implementing advanced econometric specifications (e.g., fixed effects), and executing substantial robustness checks (placebo periods, alternative clustering). It also guides the planning of reproducible numerical illustrations for theoretical models.