Skill UI

This guide provides comprehensive methodological advice for academic submissions in Information Systems (IS), particularly for journals like JAIS. It covers selecting and rigorously defending diverse research designs—including behavioral experiments, causal inference (Economics of IS), design science, qualitative studies, and literature reviews. Use it when you need to validate the suitability of your methodology against your research claims and underlying phenomena.

A comprehensive guide for designing robust, causal econometric studies using large-sample archival data. It details how to identify valid sources of variation (e.g., DiD, RD, IV), mitigate endogeneity threats (e.g., reverse causality, selection bias), and construct reproducible data pipelines required for top-tier academic research in accounting.

This comprehensive guide details the rigorous methodological standards required for validating new statistical estimators, tests, or algorithms. It moves beyond simple causal inference, emphasizing foundational proofs of identification, regularity conditions, asymptotic consistency, and finite-sample robustness. It serves as a checklist for assuring the credibility and reliability of methods in academic statistical journals.

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 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 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.

A comprehensive guide for handling endogeneity, selection bias, and causal inference in empirical finance research, particularly targeting the high standards of journals like JFE. It details advanced econometric methodologies including Difference-in-Differences (DID), Regression Discontinuity Design (RDD), and Instrumental Variables (IV), ensuring your causal claims are rigorously defensible against referee scrutiny.

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.

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.

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.

A comprehensive guide for constructing and auditing empirical data pipelines, specifically tailored for high-impact journals. It covers advanced causal inference techniques (DID, RDD, IV, RCT), detailed sample management, robustness testing, and creating structured comparative estimates to withstand intense academic scrutiny.