Skill UI

This guide provides a comprehensive methodological framework and checklist for researchers conducting quasi-experimental and micro-panel studies in rural economics. It systematically addresses common econometric pitfalls, such as self-selection bias, reverse causality, and endogeneity, detailing best practices for advanced methods like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), and Propensity Score Matching (PSM).

A comprehensive guide detailing advanced econometric methods for establishing causal relationships in policy evaluations. It covers techniques such as Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and structural modeling. The core focus is bridging the gap between econometric rigor and policy legibility, ensuring that the causal mechanism is explicit in the main text while maintaining formal defense in the appendix.

This guide teaches how to restructure complex econometric tables and figures for manuscripts intended for a dual audience of professional economists and non-technical policymakers. The focus is on clarity, interpretability, and impact, ensuring that the main text presents policy-relevant findings (e.g., event studies, welfare decomposition) while relegating dense technical details (full regression dumps, diagnostics) to the appendix. It emphasizes translating standard coefficients into meaningful units like jobs or % of GDP.

A comprehensive guide for advanced econometric techniques in causal inference, covering modern approaches for quasi-experimental designs such as staggered Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Double Machine Learning (DML). It details best practices, critical diagnostic tests (e.g., weak instruments, parallel trend checks, bias decomposition), and robust reporting standards required for high-impact economic research.

This guide helps structure, audit, and refine the exhibits (tables and figures) for academic manuscripts, particularly in Human Relations. It ensures that the inference path from raw data to theoretical claims is visible. It covers best practices for both qualitative exhibits (data-structure models, evidence tables) and quantitative exhibits (regression models, interaction plots), while adhering to submission constraints like anonymization and word limits.

This guide provides rigorous standards for authors, detailing how to convert dense statistical outputs (like complex regression coefficients or parameter estimates) into clear, single-question exhibits for top economics journals. It emphasizes shifting focus from mere statistical reporting (e.g., significance stars) to communicating core economic mechanisms, comparative statics, and parameter identification, ensuring the argument is immediately legible to a broad, expert audience.

A comprehensive methodological guide detailing how to design rigorous, publishable empirical or analytical studies for top-tier accounting and economics journals (like JAE). It focuses heavily on achieving causal identification by mastering techniques such as Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RDD), and event studies, ensuring the study design credibly supports the economic prediction.

This guide outlines the rigorous standards for preparing empirical tables and figures for top-tier academic journals, such as the Journal of Accounting and Economics (JAE). It covers the entire research workflow, including variable definition, sample construction, descriptive statistics, correlation matrices, advanced regressions, and event studies. The goal is to ensure all exhibits are self-contained, reproducible, and meet strict academic reviewer expectations.

A comprehensive guide for executing and reporting the full empirical robustness battery required by top accounting journals. It covers modern econometric techniques like standard error clustering, advanced Difference-in-Differences (DiD) estimators for staggered timing, Regression Discontinuity (RD) diagnostics, and Instrumental Variables (IV/2SLS). The guide emphasizes rigorous validation of accounting constructs, executing multiple sensitivity tests, and ensuring full data-and-code reproducibility for academic publication.

A comprehensive guide for finalizing the core evidence—tables and figures—for high-stakes empirical accounting or economics papers (e.g., Journal of Accounting Research). It outlines the mandatory 'exhibit set,' covering sample construction, descriptive statistics, correlation matrices, main regression results, and crucial identification plots (DiD, RD). Focuses on adhering to rigorous house styles and ensuring all claims are data-supported.

A comprehensive guide for stress-testing and defending empirical identification strategies in finance and economics manuscripts. It covers advanced econometric techniques such as Staggered DiD, Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Event Studies, providing actionable advice on fixed effects, clustering, necessary robustness checks, and anticipating referee challenges.

This guide provides comprehensive guidelines for preparing high-quality, publication-ready tables and figures specifically tailored for Journal of Banking & Finance (JBF) submissions. It covers everything from structuring descriptive statistics and regression results to designing event-study plots and ensuring proper notes and execution workflows.