Skill UI

A comprehensive guide for authors preparing medical manuscripts for high-impact journals. It outlines rigorous standards for statistical reporting, emphasizing the use of effect sizes with 95% Confidence Intervals (CIs) over p-values alone. Key topics include pre-specification, intention-to-treat analysis, multiplicity control, handling missing data through imputation, and reporting both relative and absolute effects to ensure maximum statistical rigor and reproducibility.

This comprehensive guide provides a structured framework for building and auditing empirical data and estimation pipelines in finance and banking research. It covers data construction from various sources (e.g., CRSP, Call Reports), advanced econometric techniques like fixed effects and winsorization, conducting stress tests (e.g., crisis periods, large banks), and ensuring full reproducibility. It emphasizes reporting results with meaningful economic magnitudes.

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.

This guide details advanced best practices for creating publication-ready, APA-formatted tables and figures for academic research, particularly in behavioral sciences. It moves beyond simply presenting numbers, focusing instead on visualizing the underlying research mechanism, such as mediation paths, moderated effects, and multi-study comparisons. Mastery ensures rigor and clarity for top-tier submissions.

This guide helps researchers proactively anticipate the critical objections raised by academic journal referees, especially in behavioral economics. It covers key areas such as demand effects, mechanism separation, external validity, and determining proper journal fit, allowing authors to significantly strengthen their drafts before formal submission.

This guide provides best practices for creating high-impact tables and figures (exhibits) for academic journals, especially in behavioral economics. It emphasizes visualizing the core behavioral comparison—such as treatment effects, dynamic changes over rounds, or underlying distributions—so the main finding is immediately visible before reading any text. Key practices include using confidence intervals over significance stars, ensuring grayscale readability, and providing self-contained captions.

This guide equips researchers with advanced strategies to anticipate and neutralize the highly specialized objections raised by environmental and resource economists. It covers core methodological pitfalls such as spatial confounding, hypothetical bias, leakage effects, sorting issues, and welfare overreach. By structuring the paper preemptively, authors can significantly increase robustness and dramatically improve the likelihood of successful publication.

This comprehensive guide provides advanced methodological strategies for authors submitting to the Journal of Economic Geography (JEG). It addresses critical challenges in establishing causal inference using spatial data, such as spatial autocorrelation, spillover effects (SUTVA violations), and structural elasticity identification. It covers modern techniques for spatial DID, Network Equilibrium Models (NEG), and qualitative case studies, ensuring methodological rigor.

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.

This comprehensive guide assists in executing and documenting rigorous empirical finance research, tailored for top-tier journals like JFQA. It covers the entire pipeline, including complex financial data construction (CRSP, Compustat), advanced data cleaning (winsorizing, outlier handling), and sophisticated econometrics (fixed effects, clustered standard errors, Fama-MacBeth). The core focus is achieving absolute reproducibility, ensuring that all filters, robustness checks, and analytical steps are meticulously documented for archival and peer review.