Skill UI

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.

A comprehensive guide for econometricians submitting manuscripts to top journals. It details how to structure analyses for full reproducibility, including using master scripts, pinning software versions, and exporting results to plain-text formats. Coverage includes advanced robust inference techniques such as HAC, cluster bootstrapping, and weak-IV diagnostics, ensuring the work satisfies rigorous archival standards.

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.

This comprehensive guide provides a structured framework for building and auditing empirical data and estimation pipelines in finance and banking research. It covers data construction from various sources (e.g., CRSP, Call Reports), advanced econometric techniques like fixed effects and winsorization, conducting stress tests (e.g., crisis periods, large banks), and ensuring full reproducibility. It emphasizes reporting results with meaningful economic magnitudes.

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.

A comprehensive guide for analyzing and reporting findings in entrepreneurial research, specifically tailored for high-level academic journals. It covers advanced econometric techniques necessary to address common data challenges in venture panel data, such as survivorship bias, selection into founding, endogeneity, and attrition. It ensures methodological rigor and robust reporting.

A comprehensive guide for conducting advanced empirical analysis required for top-tier finance journals. It covers assembling multi-source firm panels (Compustat, CRSP, etc.), constructing complex corporate finance variables, implementing advanced econometric techniques (high-dimensional fixed effects, clustered errors), and systematically planning robustness checks and replication strategies.

A comprehensive guide to establishing rigorous identification for advanced economic papers, targeting general-interest academic journals. It covers causal inference techniques (DID, IV, RDD), structural parameter identification, and theoretical mechanism rigor, ensuring the link between data/assumptions and results is explicitly defensible.

A comprehensive guide for establishing robust causal links in environmental economics. It covers advanced econometric techniques for policy evaluation, including staggered Difference-in-Differences (DiD), Instrumental Variables (IV), and Regression Discontinuity Designs (RDD). It specifically addresses challenges in non-market valuation, such as spatial sorting, hypothetical bias, and separating weather shocks from long-term climate trends, ensuring the recovered parameter is welfare-relevant.

This comprehensive guide outlines the rigorous methodology required for empirical and theoretical growth studies, particularly for high-impact journals like JEG. It covers best practices for cross-country panel data analysis, calibration techniques, spatial inference (Conley SEs), and structuring robust manuscripts. Use this checklist when estimating growth outcomes, simulating transition paths, or conducting deep sensitivity analyses for development economics.

This comprehensive guide provides advanced methodological strategies for authors submitting to the Journal of Economic Geography (JEG). It addresses critical challenges in establishing causal inference using spatial data, such as spatial autocorrelation, spillover effects (SUTVA violations), and structural elasticity identification. It covers modern techniques for spatial DID, Network Equilibrium Models (NEG), and qualitative case studies, ensuring methodological rigor.

This guide outlines a systematic methodology for gathering, reading, and synthesizing large bodies of academic research for comprehensive economic surveys (JEL style). It moves beyond simple summarization by enforcing structured data collection into a detailed evidence matrix. The process ensures maximum coverage through advanced search techniques (e.g., snowballing, JEL code sweeps) and focuses on synthesizing connections, identifying consensus, and articulating scholarly tensions across different studies.