Skill UI

This resource simulates the rigorous peer review process of a top journal, focusing on educational relevance, theoretical mechanism, design rigor (especially nested data analysis), and transparency standards (JARS, preregistration). Use this guide to stress-test your manuscript before submission, preemptively address common rejection patterns, or interpret a complex decision letter, ensuring your work meets the highest academic standards.

This skill assists researchers in creating methodologically rigorous study designs for educational psychology manuscripts, especially when dealing with nested data structures (e.g., students within classes within schools). It guides users in matching the unit of randomization, power, and analysis to the correct cluster level. Focus areas include ensuring robust cluster-level power, establishing ecological validity, developing pre-analysis plans, and adhering to preregistration standards.

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 guide equips researchers with advanced strategies to anticipate and neutralize the highly specialized objections raised by environmental and resource economists. It covers core methodological pitfalls such as spatial confounding, hypothetical bias, leakage effects, sorting issues, and welfare overreach. By structuring the paper preemptively, authors can significantly increase robustness and dramatically improve the likelihood of successful publication.

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.

A comprehensive guide for environmental economists on transforming raw analysis results into publication-grade exhibits suitable for JEEM/Elsevier. Focuses on moving beyond simple regression coefficients to visualize spatial variation (maps), temporal dynamics (event studies), and welfare changes (WTP curves), ensuring the core environmental signal is clearly communicated to referees and readers.

A comprehensive guide for academic authors on how to structure theoretical and econometric models in environmental economics. It details what constitutes a 'policy-relevant' model—one that generates testable welfare expressions, optimal policy predictions, or comparative statics that inform regulatory decisions, moving beyond mere descriptive intuition.

A comprehensive guide for authors in spatial economics on conducting rigorous robustness checks. It systematically addresses common spatial threats—such as the Modifiable Areal Unit Problem (MAUP), spatial weight matrix sensitivity, and spatial autocorrelation—to ensure published results are scientifically sound and not artifacts of the data definition or model specification. Essential for high-level academic submissions.

A comprehensive guide for authors publishing in economic geography journals, detailing how to structure manuscripts to satisfy both economic and geographical communities. It provides methodological advice on transitioning between formal NEG/quantitative-spatial models and conceptual/evolutionary frameworks, ensuring that the underlying mechanisms and assumptions are transparent and testable.

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.

Provides a comprehensive framework for constructing a robust battery of empirical tests, tailored for high-impact academic journals like JFE. This guide covers alternative measures, specifications, subsamples, and falsification tests, ensuring research findings are highly credible and capable of withstanding rigorous academic scrutiny.