Skill UI

This guide helps researchers determine if their project's core contribution is genuinely centered on recommender systems. It provides a structured fit test to route the work to the most appropriate top-tier venue (RecSys, SIGIR, KDD, etc.) and select the correct conference track before starting the writing process.

This guide outlines how to strategically frame a manuscript's contribution for journals like the Review of Economic Dynamics (RED). It emphasizes identifying a single, sharp, dynamic or quantitative advance (e.g., a new mechanism, method, or quantitative result) and presenting it as a 'delta' against the closest existing model. It provides a checklist and stress test to ensure the core finding is immediately grasped by the target readership.

This guide helps researchers test whether their economic research question aligns with the specialized scope of top-tier journals, particularly those focusing on dynamic and quantitative mechanisms (like RED). It provides a structured methodology, including fit tests and scoring dimensions (Dynamics, Model, Discipline, Audience), to ensure the core contribution is genuinely dynamic and methodologically rigorous.

This skill guides the execution and stress-testing of empirical analysis for innovation studies (Research Policy). It details how to build reproducible bibliometric and patent variables, select appropriate econometric models (like Poisson/Negative Binomial for counts), and establish robust evidence. The focus is on addressing methodological challenges, ensuring transparent variable construction, and performing rigorous sensitivity checks to withstand expert scrutiny.

This comprehensive guide details advanced econometric methodologies required for rigorous causal inference studies. It covers how to defend both the causal identification strategy (DiD, RD, IV) and the quality of variables (handling measurement error, index construction). Learn to meet the stringent standards of top-tier journals by applying techniques like staggered DiD estimators, density testing, and attenuation correction.

A comprehensive framework designed for rigorous academic research, ensuring that primary findings remain stable across various methodological choices. It systematically tests the robustness of estimates against potential weaknesses in specification, sample selection, measurement, identification, and inference. This suite defends the headline result against skeptical scrutiny, which is crucial for publication in top-tier economics journals.

A methodological guide for empirical economists on balancing theoretical depth with empirical findings. It advises authors on how to structure models—determining if a structural, light, or minimal theory dose is appropriate—to ensure the core contribution remains the testable estimate, rather than the theory itself.

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 guide provides a rigorous framework for stress-testing and implementing state-of-the-art causal identification strategies required for high-impact empirical economics. It covers advanced methodologies including Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Synthetic Control, and Structural Modeling. It emphasizes modern estimators (e.g., Callaway-Sant'Anna, effective F) and robust diagnostic checks to ensure the causal claims are methodologically sound and defensible.

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.

A comprehensive guide for executing rigorous empirical accounting studies. This resource details best practices for advanced econometric techniques, including correct standard error clustering (firm-level, two-way clustering), executing identification strategies (DiD, RD, IV/2SLS), validating accounting constructs, and performing extensive robustness checks (falsification, placebo tests). It also emphasizes data provenance and reproducibility, critical for top-tier academic publications.

This guide helps structure and build rigorous academic theories and economic models, especially for accounting manuscripts. Use it when the core challenge is articulating the underlying economic mechanism (friction, channel), building a minimal, provable model, or moving beyond simple correlation (HARKing). It guides the process from raw primitives to testable, proposition-based arguments.