Skill UI

A comprehensive guide for planning and stress-testing empirical analysis in financial intermediation, specifically for high-impact journals. It covers the construction of bank/loan-level panel datasets, defining variables (growth vs. levels), implementing advanced econometric specifications (e.g., fixed effects), and executing substantial robustness checks (placebo periods, alternative clustering). It also guides the planning of reproducible numerical illustrations for theoretical models.

A mandatory guide for authors submitting to the Journal of Financial and Quantitative Analysis (JFQA). This policy dictates the required archive structure, ensuring that all reported findings can be fully reproduced by depositing source code, raw or pseudo datasets, and a detailed execution roadmap in the JFQA Dataverse. It guarantees the highest standards of academic transparency and reproducibility in financial research.

This specialized tool guides researchers in critically evaluating if an academic idea or dataset qualifies as a genuine, theory-contributing international-business phenomenon. It implements a rigorous multi-gate test, analyzing the phenomenon, level of analysis, international theory contribution, and cross-national variation needed for high-impact publishing in journals like JIBS.

A comprehensive guide to constructing rigorous empirical datasets for academic papers in finance and banking. It covers best practices for handling complex data sources (e.g., bank micro-data, central bank reports, macro series), addressing measurement biases, and ensuring full replicability, adhering to the standards of top-tier economic journals. Focus areas include merger tracking, regime transitions, and variable definition consistency.

This guide teaches authors how to precisely position their academic contribution within existing specialized literature, particularly in finance and banking. It moves beyond vague claims by anchoring the work to canonical economic mechanisms and distinguishing the specific 'delta' (new shocks, frictions, or datasets). Mastering this skill ensures the manuscript clearly articulates its unique value and policy relevance to experts.

A comprehensive guide detailing the data sharing and transparency requirements for submitting manuscripts to the Journal of Marriage and Family (JMF). It specifically addresses the challenge of restricted-use family datasets, teaching researchers how to document access paths and ensure replication-level detail without direct data redistribution.

Provides comprehensive guidelines for authors submitting to the Journal of Econometrics (JoE). It details the required best practices for data citation (using the Elsevier `[dataset]` format), ensuring methodological reproducibility (e.g., Monte Carlo simulations), and preparing estimators as runnable code packages (R, Python, etc.). This guide is essential for meeting rigorous academic publication standards and passing peer review.

This skill guides authors on meeting stringent academic requirements for manuscript submission, specifically addressing the mandatory replicability standard set by The Journal of Politics (JOP). Acceptance is strictly contingent on providing a complete, documented, and executable package. The required materials include the dataset(s), master code, codebook, and a comprehensive readme file, ensuring that any published finding can be independently verified and reproduced by a journal analyst. It also provides guidelines for handling restricted data.

A comprehensive guide for designing and presenting rigorous experiments for submissions to the Journal of Software (JOS). It guides users through setting clear Research Questions (RQ), selecting real-world datasets and baselines, applying advanced statistical testing (effect size, significance), conducting ablation studies, and addressing threats to validity (internal, external, conceptual). This ensures the evidence strongly supports the claims, meeting top-tier academic standards.

A comprehensive tool designed for authors targeting specialized pure theory journals, such as the Journal of Economic Theory (JET). It helps assess whether a manuscript's core contribution is general, theoretical, and novel, rather than being limited to a specific application or empirical dataset. Use it to re-frame your work, check for conceptual fit, and anticipate potential desk-rejection risks based on the journal's rigorous standards for theorems, proofs, and generality.

This comprehensive skill guides authors through the specific submission requirements and editorial culture of the Journal of Machine Learning Research (JMLR). It helps authors determine if their ML method, theory paper, or dataset is appropriate for JMLR's high standards—emphasizing rigor, complete proofs, open code, and lasting scientific contribution over novelty signaling. Use this when targeting JMLR or comparing it against other top-tier ML venues.

A comprehensive guide detailing how to structure, package, and validate research artifacts for major machine learning conferences like KDD. It covers best practices for creating anonymous, reproducible repositories, handling scale claims, and providing robust evidence for different research tracks (Research, ADS, Datasets).