Skill UI

This guide outlines best practices for authors presenting theoretical and empirical results for top-tier journals like the American Economic Journal: Microeconomics (AEJ: Micro). It covers structuring propositions, building transparent numerical examples, creating schematic figures, and formatting tables (including mandated use of standard errors over significance asterisks) to ensure maximum clarity and adherence to academic publishing standards.

This guide details best practices for transforming raw econometric outputs (coefficients, significance stars) into self-contained, policy-message-carrying exhibits for top-tier economic journals (e.g., AEJ: Policy). It emphasizes replacing significance stars with standard errors (SEs) or confidence intervals (CIs), creating a 'headline exhibit' that communicates the policy effect in interpretable units, and structuring tables and figures for maximum clarity for non-academic policymakers.

This skill guides authors on condensing complex empirical research findings (tables and figures) to meet the strict formatting constraints of top academic journals like AER: Insights. It enforces a maximum of five one-page exhibits, teaches authors how to select the headline result, central diagnostics, and mechanism, and ensures adherence to specific house styles, such as reporting standard errors instead of significance asterisks, while managing word count budgets.

This guide provides strict guidelines for compiling and formatting scholarly exhibits (tables, figures, and appendices) for top-tier accounting journals, such as CAR. It ensures compliance with academic style guides, requiring variable definitions, clustered standard errors, and self-contained presentation so that the results are immediately interpretable by reviewers. It is crucial for transforming raw analysis output into submission-ready academic materials.

A comprehensive guide for authors preparing manuscripts for top economics journals (e.g., EER). It details best practices for designing self-contained tables and figures, emphasizing the correct reporting of standard errors (SEs), confidence intervals (CIs), and interpreting results cleanly. This guide ensures exhibits adhere to rigorous academic publishing standards, moving beyond simple star counts to let the magnitude of the result speak for itself.

A comprehensive guide for structuring academic empirical research projects to ensure the full reproducibility of findings. This methodology covers standard folder layouts, master scripting, data provenance documentation, and best practices required for passing rigorous peer review scrutiny, thereby preventing data loss and calculation errors during revisions.

This guide provides strict guidelines for structuring and presenting empirical results (tables and figures) in financial management manuscripts. It emphasizes making the main coefficient and its practical economic magnitude legible at a glance, adhering to high-standard journal norms (e.g., Wiley/FMA style). It covers best practices for event-study plots, IV tables, and ensuring standard errors and economic significance are always visible.

Convert genomic intervals between formats like BED, VCF, and GFF, handling 0-based and 1-based conventions. Normalize variants, align indels, and detect assembly mismatches to prevent analysis errors. Essential for bioinformatics pipelines requiring precise coordinate mapping across different genome builds and file types.

This skill provides a comprehensive framework for academic writers aiming to submit manuscripts to specialized international economics and trade journals. It helps users assess their paper's alignment with the target journal's scope, key research themes (e.g., open economy, GVCs, cross-border e-commerce), required methodology (e.g., empirical models, international comparability), and structural standards. It is crucial for preventing manuscript rejection due to scope mismatch or formatting errors.

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.

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.

A comprehensive guide for formatting and creating high-impact tables and figures for academic submissions, particularly adhering to The Journal of Law and Economics (JLE) standards. It teaches how to visually present the main effect of a legal rule (e.g., event-studies, RD, DiD) clearly and concisely, ensuring the core identification is legible at a glance. Covers best practices for standard errors, clustering, and self-contained academic notes.