Skill UI

This guide provides a comprehensive framework for rigorously identifying and defending the methodological core of academic papers in finance and banking. It covers both empirical tracks (e.g., staggered DID, RDD, causal design for bank data) and theoretical tracks (defining necessary assumptions and analytical discipline). It helps researchers move beyond default methods to address complex challenges like internal versus external validity and causality separation.

This guide provides advanced econometric methodologies crucial for establishing causality in financial and macro research, particularly when analyzing policy shocks (e.g., LPR changes, regulatory reforms). It details the correct application of Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and high-frequency analysis, ensuring robust empirical design for top-tier journals.

This guide provides a structured framework for authors responding to reviewers' feedback for top-tier academic journals, especially in finance or economics. It emphasizes addressing methodological concerns (identification, causality, mechanisms) point-by-point, ensuring every response is backed by new evidence, and precisely locating changes in the revised manuscript.

A comprehensive guide for structuring marketing research designs, particularly for journals like the Journal of Marketing (JM). It teaches researchers how to select the most appropriate method (e.g., field experiment, survey, panel data, qualitative study) that directly addresses a substantive, managerially relevant claim. The focus is on achieving both internal validity (establishing causality) and external validity (realism in the market), ensuring the method serves the core business insight rather than being an end in itself.

This comprehensive guide assists researchers in strengthening the methodological rigor of communication studies manuscripts. It details best practices for experimental design, surveys, content analysis, and computational pipelines, helping authors preemptively address common reviewer critiques regarding causality, sample validity, and internal/external validity.

A comprehensive guide for structuring a point-by-point rebuttal letter to highly selective academic journals. It addresses core methodological concerns such as parameter sensitivity, out-of-sample evaluation, CGE reproducibility, and data transparency. It also provides strategic advice on re-framing causality claims into robust measurement contributions.

A comprehensive workflow guide designed for submitting manuscripts to The Journal of Quantitative & Technological Economics (JQTE). This router determines the optimal sequence of specialized skills (e.g., topic selection, measurement, econometrics, forecasting, rebuttal) based on the manuscript's current stage or contribution type. It helps researchers structure their work to meet the journal's specific requirements, emphasizing measurable methods and reproducibility over pure narrative causality.

A comprehensive guide designed to help researchers rigorously assess whether their academic manuscript’s core causality relies on international or open-economy dimensions. It provides clear criteria and decision trees to determine if a paper is genuinely suited for top-tier world economy journals, or if it should be redirected to specialized domestic economics publications.

A comprehensive guide for conducting and reporting rigorous experimental research in social and behavioral sciences. It covers various design types (vignette, lab, field), statistical requirements (effect sizes, power analysis), quality control measures (manipulation checks, confound control), and advanced techniques for establishing causality, ensuring high internal validity for academic publication.

This comprehensive framework guides the rigorous design and defense of research in public administration. It covers essential causal inference methodologies, including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RD), and various experimental designs on bureaucrats and citizens. Use this guide when defining identification, responding to reviewer critiques regarding causality, or pre-registering a study, ensuring the design is both academically sound and practically relevant to public policy.

A comprehensive guide for establishing credible causal inference in accounting and finance research. This methodology covers advanced techniques including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RDD), and experimental design. It instructs users on how to move beyond simple controls, defining the specific identifying variation necessary for publishing rigorous, causality-focused academic work.

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.