Skill UI

A comprehensive guide for designing and stress-testing asset-pricing empirical models, tailored for submissions to top-tier finance journals. Covers methodologies like Fama–MacBeth regressions, factor model comparisons, handling standard-error subtleties, and rigorous out-of-sample validation. Essential for ensuring results survive academic scrutiny, addressing issues like multiple testing and economic magnitude.

This guide provides best practices for designing tables and figures for economics manuscripts, specifically following the conventions required by journals like JLEO. It emphasizes foregrounding the institutional or organizational comparison, ensuring exhibits are self-contained, and converting implied arguments (like comparative statics or event studies) into visible, persuasive graphical evidence.

This guide establishes rigorous methodological standards for incorporating forecasting and prediction claims in academic manuscripts. It emphasizes the necessity of genuine out-of-sample evaluation, comparison against robust benchmarks (like Random Walk or AR models), and utilizing advanced statistical tests (e.g., Diebold-Mariano) to prove prediction superiority, moving beyond simple in-sample fit reporting.

This guide provides essential knowledge for selecting and defending data sources for advanced research in international economics. It emphasizes the critical need for data to possess a 'global attribute,' covering trade flows, Global Value Chains (GVC), and cross-country comparisons. Key data types include micro-level customs data (firm-product-destination), cross-country panels (WDI, IMF), and global input-output tables (WIOD/ADB MRIO) for GVC decomposition.

This guide provides methodological standards for defending the empirical design of a language manuscript. It covers various subfields—including fieldwork, corpus construction, phonetics, and typological comparison—ensuring that the evidence base is transparent, rigorous, and directly supports the stated theoretical claims. Use this when justifying data collection methods or addressing reviewer concerns about evidence validity.

This comprehensive guide instructs researchers on how to design rigorous and credible evaluations for Machine Learning systems. It covers critical areas such as workload selection, baseline comparison discipline, reporting four key coordinates (throughput, latency tails, memory, cost), structuring ablations, and generating scaling evidence. It ensures measurements meet academic benchmarking standards.

This guide provides an expert methodology for designing and auditing Natural Language Processing (NLP) experiments for top-tier academic submissions. It instructs researchers on how to map meta-review claims to measurable evidence, ensure budget-fair comparisons, and systematically address potential reviewer probes (scope, fairness, contamination, etc.) to maximize publication rigor.

A comprehensive guide for qualitative theory-building, covering multi-case comparison, grounded theory, and advanced methodological rigor. Learn how to structure your study by defining case selection logic, conducting multi-level coding (open, axial, selective), ensuring theoretical saturation, and establishing trustworthiness through triangulation, member checking, and audit trails. Ideal for papers built inductively from interviews or archives.

A comprehensive guide for rigorously positioning an Operations Research (OR) manuscript against prior art. It teaches authors to move beyond vague 'gap' statements by constructing a precise, technical delta against 3-5 closest papers. Focus areas include weakening assumptions, improving bounds, enhancing algorithmic complexity, and defining new operational regimes, often presented in a structured comparison table to satisfy technical reviewers.

A comprehensive guide for constructing rigorous and structured exhibits (tables, figures, theorems) for Operations Research (OR) manuscripts, adhering to INFORMS house style. It details requirements for formal statement layout, comparison tables against prior work, computational results, and performance plots, ensuring maximum academic rigor and reproducibility.

This guide details how to structure and write the 'Related Work' section for highly competitive academic venues like PLDI. It emphasizes moving beyond simple citation listings by forcing the author to identify and articulate the specific technical 'delta' (improvement) their paper provides over every prior technique, while also covering inter-conference comparisons and proper bibliography formatting.

This guide provides comprehensive standards for achieving statistical rigor in scientific research reporting. It mandates that every quantitative claim must include effect sizes, uncertainty, sample size (n), and justified statistical tests. Key areas covered include distinguishing biological vs. technical replication, conducting power analyses, implementing multiple comparison corrections, and utilizing Registered Reports to prevent publication bias, ensuring maximum transparency and reliability in scientific findings.