Skill UI

Mandatory package structure for submitting accepted empirical papers to the Quarterly Journal of Economics (QJE). It ensures complete research reproducibility by organizing raw data, derived files, and analysis code. This package guides authors in creating a comprehensive, self-contained deposit that allows any investigator to regenerate every figure, table, and result published in the manuscript, adhering to strict academic data-availability policies.

A comprehensive framework for designing, auditing, and reporting empirical results in recommender systems research. It covers best practices for handling temporal splits, mitigating evaluation bias (e.g., exposure/popularity), utilizing advanced off-policy estimators (IPS, SNIPS), and structuring the critical link between offline metrics and real-world A/B test results. Essential for academic submissions.

This guide details the strategic placement of supplementary evidence (e.g., tuning grids, ablation studies, code) for academic submissions, particularly in conference formats like RecSys. It clarifies the difference between material belonging in the main body, the page-counted appendix, and the anonymous repository, ensuring that critical findings are visible and adhere to strict page budget constraints.

A comprehensive project management skill designed for academic submissions in Recommender Systems (RecSys). It guides users through the entire research lifecycle, from initial venue fit and data freezing to abstract submission, full paper writing, anonymous repository management, rebuttal drafting, and final camera-ready preparation. It provides backward-planning time offsets and risk identification.

This guide provides a framework for selecting and defending the appropriate research methodology for innovation-studies claims. It covers best practices for patent/bibliometric analysis, causal policy evaluation (e.g., DID, RDD), survey design, case studies, and mixed methods. It emphasizes that the method must fit the construct, not merely the indicator, guiding users to defend the underlying theoretical mechanism.

This guide provides best practices for building self-explanatory, high-impact exhibits for interdisciplinary academic manuscripts. It covers the structured design of complex visualizations, including regression tables, bibliometric maps, event-study plots, and qualitative data structures. The goal is to ensure every figure and table carries the core innovation mechanism and is immediately interpretable by diverse readers (e.g., policymakers, managers, and economists).

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.

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 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 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.

A comprehensive guide for structuring rigorous empirical financial research papers, particularly for top-tier journals like RFS. It details critical design choices for sample construction, variable measurement (proxy validity), estimator selection (GMM, Fama–MacBeth, DID), and standard error adjustments, ensuring methodological rigor and reproducibility against academic scrutiny.

A comprehensive guide for authors addressing the rigorous identification requirements for industrial-organization (IO) manuscripts, particularly targeting top-tier journals like the RJE. It details structural and reduced-form modeling approaches for areas including demand, supply, entry, mergers, and auctions. The resource emphasizes defending endogeneity, selecting appropriate market-based instruments, utilizing modern econometric estimators (e.g., Callaway–Sant'Anna), and establishing clear causal links between variation and structural parameters.