Skill UI

A comprehensive guide to designing highly defensible and causal research studies for top-tier finance journals, such as the Journal of Financial and Quantitative Analysis (JFQA). It covers advanced econometric methods for corporate finance, asset pricing, and policy shocks, including Fixed Effects, Staggered Difference-in-Differences (DID), Instrumental Variables (IV), and Regression Discontinuity Design (RDD). Focuses on addressing endogeneity, reporting economic magnitude, and ensuring robust inference.

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 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 helps researchers structure the institutional background section of advanced finance manuscripts. It emphasizes moving beyond general descriptions to precisely articulate regulatory rules, market segmentation, and ownership structures. The goal is to transform descriptive text into clear evidence for identifying exogeneity and analyzing policy shocks in empirical studies.

A comprehensive guide for rigorous causal inference in health policy research. It details advanced econometric methodologies—including Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), and Instrumental Variables (IV)—and addresses critical issues like selection bias and institutional realism. This resource helps ensure that empirical claims meet the highest standards required by top-tier health economics journals.

Provides a comprehensive framework for conducting and reporting robustness checks in health economics studies. It guides authors to organize findings by addressing specific threats to the core claim (e.g., selection bias, concurrent policy changes, measurement error) rather than simply appending multiple specifications. Essential for ensuring results are stable and defensible for top-tier journal submissions.

A comprehensive guide detailing best practices for constructing rigorous and legible tables and figures for health economics manuscripts, particularly for the Journal of Health Economics (JHE). It covers presentation requirements for main policy estimates, identification figures (e.g., event studies, RD plots), and descriptive institutional exhibits, ensuring that the health setting and causal effects are immediately apparent to expert readers.

A comprehensive guide for constructing and applying structural models in health economics, particularly for interpreting empirical estimates or running policy counterfactuals. It covers core archetypes—including demand-selection, provider incentives, and human capital—and emphasizes critical rigor, such as parameter identification, adherence to institutional rules (e.g., risk adjustment), and defending policy-invariance (Lucas critique).

This guide helps authors structure and strengthen their contribution claims for top-tier journals in economics and human resources. It emphasizes moving beyond simply stating new evidence by focusing on policy relevance, translating coefficients into natural human-capital units (like dollars or percentage points), and rigorously reconciling findings with prior literature to maximize impact.

A comprehensive guide for stress-testing causal identification in empirical microeconometrics. It covers advanced methods like RCT, DID, RDD, and IV, emphasizing robustness checks, identifying selection bias, and structuring results for clear public policy interpretation in high-impact academic journals.

This comprehensive guide outlines the mandatory data and replication policies for submissions to the Journal of Human Resources (JHR). It details best practices for creating robust submission packages, including Data Availability Statements, code packages, and plans for public repository deposit. It ensures maximum reproducibility and adherence to CC0 licensing standards for academic research.

This tool guides researchers on whether their academic topic aligns with the scope of an empirical microeconomics journal, specifically the Journal of Human Resources (JHR). It emphasizes that the research must focus on policy-relevant, causal questions in core fields (e.g., labor, health, education), strictly differentiating it from purely organizational behavior or 'HR management' studies. It serves as a critical gatekeeper before formal submission.