Skill UI

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.

A comprehensive methodological guide detailing the robustness checks expected by environmental economics journals (e.g., JEEM). It provides a checklist of targeted statistical tests—including spatial inference, handling omitted variables, and varying market definitions—to ensure key welfare estimates are stable and not artifacts of specification choices. Essential for academic research in environmental and resource economics.

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.

A comprehensive methodological guide for conducting rigorous quantitative research in finance, covering asset pricing, corporate finance, and econometrics. This skill defines the necessary standards for factor construction, portfolio sorting, advanced estimators (Fama-MacBeth, GMM), robust standard error clustering, and multiple-testing discipline. It ensures research designs are academically sound, reproducible, and suitable for top-tier financial journals.

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 detailed guide for constructing a comprehensive robustness ledger in empirical financial research, particularly for microstructural data. It structures checks to address common academic threats, such as measurement bias, sample selection, microstructure noise (e.g., bid-ask bounce), and flawed inference, ensuring findings are methodologically sound and highly defensible in top-tier journals.

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 specialized guide for authors submitting manuscripts to high-impact academic journals like JFQA. It provides a structured methodology for addressing detailed concerns from double-anonymous referees and the managing editor, covering advanced topics such as endogeneity, robust inference, economic magnitude, and ensuring full reproducibility for final acceptance.

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.