Skill UI

This guide helps structure, audit, and refine the exhibits (tables and figures) for academic manuscripts, particularly in Human Relations. It ensures that the inference path from raw data to theoretical claims is visible. It covers best practices for both qualitative exhibits (data-structure models, evidence tables) and quantitative exhibits (regression models, interaction plots), while adhering to submission constraints like anonymization and word limits.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.

This comprehensive guide provides rigorous econometric strategies for establishing parameter identification, crucial for structural modeling and causal inference. It details how to prove that model parameters are uniquely determined by the observed data, moving beyond mere estimation convergence or external calibration. Essential for advanced empirical research and addressing stringent peer review standards.

This guide provides comprehensive methodological advice for academic submissions in Information Systems (IS), particularly for journals like JAIS. It covers selecting and rigorously defending diverse research designs—including behavioral experiments, causal inference (Economics of IS), design science, qualitative studies, and literature reviews. Use it when you need to validate the suitability of your methodology against your research claims and underlying phenomena.

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.

A comprehensive guide detailing the best practices for drafting a rebuttal letter in response to single-blind peer reviews for applied econometrics journals. Covers critical areas like defending identification, improving inference, and ensuring robustness, while maintaining mandatory synchronization between the revised manuscript and the official data archive.

Provides a comprehensive guide to the Journal of Applied Econometrics (JAE) submission and review process. It details the single-blind peer review structure, the final decision authority of the Editor-in-Chief, and the high standards for empirical rigor, including data reproducibility, appropriate inference methods, and mandatory data archiving. Essential for authors planning submissions and resubmissions.

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.

This comprehensive guide details the rigorous methodological standards required for validating new statistical estimators, tests, or algorithms. It moves beyond simple causal inference, emphasizing foundational proofs of identification, regularity conditions, asymptotic consistency, and finite-sample robustness. It serves as a checklist for assuring the credibility and reliability of methods in academic statistical journals.

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 rigorous empirical analysis on field and survey data in low- and middle-income settings. It covers advanced methods for handling clustered sampling, measurement error, differential attrition, and treatment-effect heterogeneity, ensuring results meet the high standards of reproducibility and robustness required by leading developmental economics journals.

A comprehensive guide to establishing rigorous identification for advanced economic papers, targeting general-interest academic journals. It covers causal inference techniques (DID, IV, RDD), structural parameter identification, and theoretical mechanism rigor, ensuring the link between data/assumptions and results is explicitly defensible.