Skill UI

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 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.

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 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.

This comprehensive guide provides meticulous standards for presenting quantitative empirical results, particularly for academic journals like JHR. It covers the structure for main estimates, design diagnostics (pre-trends, balance), reconciliation tables, and organizing the online appendix. Learn how to maximize clarity and impact while adhering to strict page limits.

This guide provides a structured methodology for designing a comprehensive robustness suite for high-stakes academic papers, particularly in monetary economics and banking. Instead of running arbitrary checks, it maps diagnostic tests directly to named econometric threats (e.g., shock contamination, specification dependence, inference understatement, regime shifts), ensuring the credibility and rigor of empirical findings.

A comprehensive guide detailing the quantitative and econometric standards required for submissions to top-tier monetary and macroeconomic journals. It covers advanced techniques like VAR/SVAR, Local Projections, and DSGE modeling, focusing on proper inference, robustness checks, and diagnostic reporting to ensure scholarly rigor.

This guide provides a rigorous structural and strategic framework for responding to a 'Revise and Resubmit' (R&R) decision, specifically tailored for the Journal of Labor Economics (JOLE). It emphasizes drafting concise, point-by-point rebuttals, addressing complex identification challenges with robust diagnostics, reconciling conflicting referee demands, and strictly adhering to the journal's word economy and single-blind submission rules. It is a critical tool for refining advanced academic manuscripts.

This router guides authors through the entire process of preparing a manuscript for the Journal of Labor Economics (JOLE). It does not replace specialized skills but serves as a diagnostic tool, telling the user which specific jole-* skill to invoke next based on the current writing bottleneck, from initial topic selection through to rebuttal and final submission.

This skill guides researchers in developing robust, theoretically innovative manuscripts, specifically for top-tier journals like JPSP. It moves beyond simply reporting data by teaching how to define a core claim, derive diagnostic, testable hypotheses (H1, H2, H3...), establish clear mechanisms, and map the cumulative argument across multiple studies. It focuses on the theoretical framework, not the data analysis itself.

This guide provides a rigorous framework for advanced econometric modeling in quantitative economics. It covers time series, panel data, cointegration, and mixed-frequency techniques (VAR, VECM, GMM, MIDAS). It emphasizes proper model diagnostics, stationarity testing, and grounding models in specific data properties and economic contexts.

A comprehensive skill for advanced meta-analysis in psychology, designed to assess the reliability and sources of heterogeneity. It performs moderator/subgroup analyses (meta-regression) to explain effect size variation and runs multiple publication-bias diagnostics (e.g., Funnel plot, Egger's test, p-curve, selection models). Essential for rigorous academic reporting requiring robust evidence and sensitivity checks.