Skill UI

Comprehensive guidelines for preparing and reporting complex empirical evidence across various methodologies (SEM, econometrics, design science, qualitative). This guide ensures results meet the high academic standards of top-tier journals by emphasizing theoretical traceability, statistical rigor, and required data transparency, including full correlation/covariance matrices.

This comprehensive guide helps researchers evaluate whether a clinical or health-services study is appropriate for publication in JAMA Internal Medicine. It details the journal's high bar for methodological rigor, including requirements for randomized trials (CONSORT), observational studies (STROBE), and systematic reviews (PRISMA). It covers everything from study design and statistical reporting to addressing potential desk-reject triggers, ensuring authors submit high-quality, impactful research.

A comprehensive guide for authors to select, defend, and strengthen the internal validity of clinical study designs (RCT, Cohort, Diagnostic, etc.). It provides detailed safeguards—covering topics like intention-to-treat analysis, confounding control, blinding, and power calculations—to ensure manuscripts meet the rigorous reporting standards of top-tier medical journals like JAMA.

A comprehensive guide for structuring and validating empirical research designs (Survey/SEM, Secondary Data, Experiments, Meta-analysis) intended for high-impact journals. It addresses critical methodological issues such as construct validity, endogeneity control, robust identification strategies, and ensuring overall rigor across diverse academic genres.

This guide details the rigorous, reproducible empirical strategies required to identify causal parameters in applied econometrics, meeting high standards (e.g., JAE). It covers various designs—including DID, IV, panel data, and time-series—and mandates that every assumption and diagnostic test must be explicitly stated, tested, and reproducible using deposited code/data.

A comprehensive orchestrator skill guiding the entire lifecycle of an applied econometrics manuscript targeting the Journal of Applied Econometrics (JAE). It ensures the paper maintains an applied, real-data focus and rigorously follows the necessary steps, including literature positioning, identification strategy, reproducible data analysis, and mandatory data archival deposit. Designed to prevent common pitfalls by integrating data integrity checks at every stage.

A comprehensive guide for designing robust, causal econometric studies using large-sample archival data. It details how to identify valid sources of variation (e.g., DiD, RD, IV), mitigate endogeneity threats (e.g., reverse causality, selection bias), and construct reproducible data pipelines required for top-tier academic research in accounting.

A comprehensive guide designed for authors submitting to the Journal of Business & Economic Statistics (JBES). It details the unique multi-Co-Editor model, how referees scrutinize submissions (focusing on statistical validity, empirical relevance, and reproducibility), and the conventions of the discussion-paper tradition. This resource helps authors set expectations and anticipate critiques before drafting the formal response letter.

A comprehensive preflight checklist designed for submitting manuscripts to the Journal of Business & Economic Statistics (JBES). This guide ensures authors have verified all necessary components—including the data/code supplement, conflict disclosures, and cover letter targeting—against the latest academic standards. It functions as a critical final review step before formal submission, emphasizing the need to cross-reference live journal guidelines.

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.

This guide provides comprehensive guidelines for preparing high-quality, publication-ready tables and figures specifically tailored for Journal of Banking & Finance (JBF) submissions. It covers everything from structuring descriptive statistics and regression results to designing event-study plots and ensuring proper notes and execution workflows.

A comprehensive guide for designing and defending the causal identification strategy in empirical corporate finance research. It covers advanced methods such as Staggered DID, Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Event Studies, providing checklists to address crucial econometric threats like endogeneity, selection bias, and reverse causality for high-impact publications.