Skill UI

This comprehensive framework guides the rigorous design and defense of research in public administration. It covers essential causal inference methodologies, including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RD), and various experimental designs on bureaucrats and citizens. Use this guide when defining identification, responding to reviewer critiques regarding causality, or pre-registering a study, ensuring the design is both academically sound and practically relevant to public policy.

A comprehensive guide and heuristic tool for assessing whether an academic manuscript is suitable for high-level public administration and policy journals, specifically referencing Public Administration and Policy Review. It details required scope, suitable methodologies (e.g., policy evaluation, case studies, mixed methods), structural requirements (anonymity, abstract formatting), and common rejection pitfalls to guide authors toward rigorous academic writing.

A comprehensive guide to the rigorous academic peer review process, exemplified by top-tier Recommender Systems conferences (RecSys). It details the double-blind review mechanics, key evaluation criteria (novelty, validity, reproducibility), the critical rebuttal phase, and the requirements for final publication in ACM Digital Library. Essential for researchers and students aiming to improve academic submission quality.

This guide outlines best practices for creating quantitative exhibits in dynamic macroeconomics papers. It covers crucial components like Impulse-Response Functions (IRFs), calibration tables, moment-matching results, and policy counterfactuals. The focus is on ensuring the model's mechanism and quantitative fit are clear, legible, and self-contained for expert review.

This strategy guides researchers in anticipating the specific, rigorous methodological objections raised by top-tier academic referees, especially in applied econometrics. It provides a systematic map connecting econometric designs (like DID, IV, RD) to common threats (e.g., measurement error, bias, specification issues). Users can pre-empt these criticisms by structuring their manuscript to include necessary defenses, robustness checks, and scope limitations before formal submission.

This guide outlines the comprehensive policy and architecture required for submitting a fully reproducible replication package to The Review of Economics and Statistics (REStat). It details how to structure the repository, including data, master scripts, and the README file, ensuring that every result can be traced and regenerated by other researchers.

This specialized router guides the entire manuscript development process for submissions to The Review of Economics and Statistics (REStat). It does not replace specialized skills but systematically directs users through the full research pipeline—from initial topic selection and causal identification to robustness testing, figure creation, and rebuttal strategy—ensuring a structured approach to applied econometrics research.

This skill guides authors on how to proactively strengthen their academic manuscripts before submission, especially for highly demanding journals. It involves identifying the most likely expert reviewers, predicting their specific methodological, theoretical, or empirical objections, and strategically integrating countermeasures, robustness checks, and alternative explanations directly into the main text. It transforms the submission process from a defensive reaction to a proactive, structured argument.

This guide details how to assemble a submission-ready replication package for top academic journals, such as The Review of Economic Studies (REStud). It covers the necessary repository structure, mandatory README sections, and coding best practices (e.g., master scripts, relative paths, version pinning) to ensure that the data and code can be fully reproduced and verified by journal editors before publication.

This guide provides a comprehensive methodological framework for conducting and reporting rigorous robustness checks in high-stakes empirical economics papers. It details crucial steps—including specification stability, placebo tests, inference corrections, and mechanism evidence—necessary to anticipate skeptical reviewers. The goal is to harden findings, ensure reproducibility, and elevate the credibility of research findings.

This comprehensive guide outlines the rigorous standards for creating publication-grade tables and figures for top-tier academic journals, such as The Review of Economic Studies. It emphasizes the principles of elegance, economy, and self-containment. Authors must ensure that every exhibit carries one single, clear result, presents interpretable magnitudes (not just stars), and is generated programmatically from code to guarantee reproducibility and consistency.

A comprehensive guide for tackling a 'revise-and-resubmit' academic review (R&R). This process details how to substantively resolve critical, first-order concerns—such as identification threats, construct measurement, or model assumptions—before drafting the letter. It emphasizes rigorous analysis, structured point-by-point responses, maintaining manuscript consistency, and preserving anonymity for resubmission.