Skill UI

This guide outlines the rigorous methodological standards required for empirical finance manuscripts. It covers essential details from sample construction (attrition tables, survivorship bias) to variable definition, merge integrity, and appropriate statistical inference (clustering, panel structure). Use this to harden your data layer and ensure your methodology is robust enough to pass peer review in top finance journals.

This guide outlines advanced econometric strategies and best practices for establishing causal identification in corporate finance and asset pricing. It addresses common methodological pitfalls, such as endogeneity, spurious correlation, and weak designs, providing specific techniques (e.g., staggered DiD, robust IV inference) required to defend a research paper's claims to top-tier academic journals.

This guide instructs authors on crafting highly targeted and convincing robustness sections for empirical finance papers. Instead of merely adding volume, it teaches the concept of 'subtraction, then precision'—identifying specific alternative explanations (threats) a referee might raise (e.g., reverse causality, omitted variables) and selecting the minimal, most effective check to neutralize each threat. This aligns with the philosophy of top-tier financial journals that value new ideas over superficial testing.

This guide provides strict guidelines for structuring and presenting empirical results (tables and figures) in financial management manuscripts. It emphasizes making the main coefficient and its practical economic magnitude legible at a glance, adhering to high-standard journal norms (e.g., Wiley/FMA style). It covers best practices for event-study plots, IV tables, and ensuring standard errors and economic significance are always visible.

A comprehensive guide for authors preparing manuscripts for Global Change Biology (GCB). It details the requirements for scientific exhibits, mandating a mechanism-first approach that clearly links environmental drivers to biological responses. Covers graphical abstract construction, ensuring visible uncertainty, and maximizing data accessibility for publication.

Provides rigorous guidelines for analyzing experimental game data and constructing numerical examples for high-level theoretical journals. Emphasizes that analysis must be tightly linked to theoretical claims, such as matching observed outcomes to predicted equilibria, rather than serving as general causal evidence. Covers session-level clustering, structural fitting, and ensuring computational reproducibility.

This guide assists authors planning to submit to Games and Economic Behavior (GEB) by detailing best practices for data and code sharing. It clarifies that while data deposition is strongly encouraged for reproducibility, it is not a mandatory requirement. It outlines what to prepare, including raw data, analysis scripts, experimental instructions, and proper availability statements.

This skill provides a rigorous academic review framework for determining if a research paper significantly advances the frontiers of game theory and its applications. It assesses conceptual advances, mechanism design robustness, behavioral novelty, and cross-field reach, guiding authors on whether their work meets the high standards of top-tier academic journals like GEB. Ideal for assessing high-level theoretical or empirical contributions.

This guide outlines the best practices and norms for executing and reporting empirical analyses in governance and comparative political science. It covers essential methods including cross-national inference, clustering uncertainty, robustness checks, multi-method triangulation, and ensuring reproducibility, guiding researchers to conduct rigorous, publication-ready studies.

A comprehensive methodological guide for structuring and defending empirical research designs in the fields of governance, institutions, and public policy. It covers advanced techniques including causal inference (DiD, IV, RDD), comparative case selection, process tracing, and mixed-methods integration. The skill emphasizes moving beyond mere description to robustly identifying contributions and ruling out rival institutional explanations.

This guide instructs authors on creating mandatory Data Availability Statements and building comprehensive replication materials for academic manuscripts. It covers best practices for data provenance, code management, and handling restricted data, ensuring research integrity and reproducibility.

This comprehensive guide outlines the process for packaging artifacts from Human-Robot Interaction (HRI) research. Recognizing that HRI artifacts are complex, involving human subjects, robot behaviors, and extensive study materials, this skill provides guidelines for honest, ethical, and reproducible dissemination. It covers structuring de-identified data, analysis code, and behavioral specifications while adhering to participant consent and academic archiving standards.