Skill UI

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 methodological guide detailing high standards for causal inference in finance research, specifically tailored for top journals like RFS. It covers advanced techniques such as Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and asset-pricing identification, ensuring designs meet rigorous standards for exogenous variation and endogeneity control.

A comprehensive guide for executing and stress-testing empirical analyses in Industrial Organization (IO). It covers structural and reduced-form model diagnostics, advanced inference techniques (e.g., wild-cluster bootstrap), counterfactual validation, and meeting the rigorous robustness standards demanded by top-tier academic journals. Focuses on methodology, not study design.

This guide provides a comprehensive strategy for elevating the credibility of finance manuscripts to the standard of top-tier journals. It details rigorous methods for empirical causal identification (DID, IV, RDD, natural experiments) and ensures theoretical rigor by assessing assumptions, generalizability, and proof exposition. It helps researchers stress-test their work's core logic.

This skill bridges theoretical econometric designs (DiD, IV, RDD, SCM, DML) to concrete, audited statistical estimates. It orchestrates the necessary tool calls to fit models, generate comprehensive diagnostics, and report reliable findings, ensuring that every academic claim is backed by executable, traceable code. Essential for advanced causal inference research.

This skill guides authors through the complex process of selecting the optimal academic journal venue for their research. It profiles the paper's characteristics (discipline, method, contribution) and shortlists potential targets from a large index. It scores candidates based on fit, acceptance odds, turnaround time, and audience, providing a tiered recommendation (Reach, Match, Safe) and a detailed resubmission ladder. Ideal for rejected drafts or papers needing a first target.

A specialized guide for researchers assessing manuscript suitability for securities and capital market journals. It analyzes the journal's positioning, preferred topics (e.g., market regulation, corporate governance, asset pricing), required methodologies (e.g., DID, event studies), and common submission pitfalls. Use this skill to structure your paper and improve its fit before formal submission.

A comprehensive guide on methodological rigor for submitting manuscripts to Social Forces. It covers designing causal inference (panel/DID/IV/RDD), demographic analysis, comparative-historical case selection, and network/computational methods. The focus is on strengthening the design by rigorously defending assumptions and ruling out alternative explanations, ensuring methodological soundness beyond mere association.

A comprehensive guide for addressing endogeneity, selection bias, and reverse causality in empirical research, specifically tailored for top-tier journals. It details advanced econometric methods (DID, IV, PSM, Heckman) and outlines best practices for conducting rigorous robustness checks, ensuring that identified effects are genuinely causal, not merely correlated.

This comprehensive guide provides methodological frameworks for designing rigorous empirical studies in strategic management. It covers defining the sample, selecting the unit of analysis, validating key measures (performance and strategic choices), and critically, implementing robust identification designs (e.g., DID, IV, Fixed Effects) to address endogeneity and establish causal claims.

This guide provides expert best practices for structuring and refining empirical tables and figures for top-tier academic journals. It teaches authors how to make exhibits self-contained, report complex econometric results (like DID and identification checks), and visualize nuanced mechanisms, ensuring maximum scholarly impact.

A comprehensive guide for conducting and reporting rigorous empirical analysis in academic research, particularly in finance and accounting. It covers selecting appropriate estimators (e.g., DiD, 2SLS, RDD), handling fixed effects, ensuring statistical robustness through placebo and alternative testing, and fulfilling strict data-authenticity and code-access requirements required by top-tier journals.