Skill UI

A comprehensive guide to constructing rigorous empirical datasets for academic papers in finance and banking. It covers best practices for handling complex data sources (e.g., bank micro-data, central bank reports, macro series), addressing measurement biases, and ensuring full replicability, adhering to the standards of top-tier economic journals. Focus areas include merger tracking, regime transitions, and variable definition consistency.

This guide provides a rigorous framework for constructing formal academic models, tailored for top-tier management journals. It emphasizes the four essential components—decision variables, constraints, objective functions, and assumptions—while providing detailed guidelines on ensuring model completeness, mathematical solvability, and, most critically, the defensibility of every assumption against academic scrutiny.

This skill provides a rigorous framework for designing academic studies, particularly for high-impact psychological journals. It guides researchers to proactively address common methodological pitfalls such as common-method variance (CMV), unmodeled nesting, and construct validity gaps. Topics covered include temporal separation, multi-level modeling, advanced causal inference techniques (DiD, IV), and pre-registration best practices, strengthening the plan before data collection.

A comprehensive guide for defending the methodological rigor of academic manuscripts, particularly for top-tier journals. It covers advanced causal identification techniques (DID, IV, RDD) for quantitative work, robust experimental design, and detailed strategies for process tracing and case selection in qualitative research. The core focus is ensuring the design is highly reproducible and clearly addresses potential confounding variables and rival explanations.

This skill guides authors in evaluating if their climate research—focusing on dynamics, variability, or change—meets the high scientific standards of the Journal of Climate. It covers scope fit, rigorous model evaluation, statistical significance testing (e.g., handling autocorrelation), and adherence to AMS house style, ensuring the contribution advances fundamental climate understanding.

A comprehensive guide to defending causal claims in policy and program evaluation, adhering to rigorous econometric standards (e.g., JPAM). It covers advanced methods such as RCT, Difference-in-Differences (DiD), Regression Discontinuity (RDD), Instrumental Variables (IV), and Synthetic Control. This guide emphasizes stating the estimand, defending identification assumptions, and robustly ruling out alternative causal explanations, rather than just writing estimation code.

This guide provides a rigorous framework for evaluating whether a research question is suitable for high-impact policy journals (like JPAM). It trains users to structure ideas by confirming three critical elements: establishing a specific, policy-relevant intervention (the policy), identifying the decision-maker and the counterfactual choice (the decision), and confirming a credible, causal design (exogenous variation). It ensures the research has both empirical rigor and real-world policy stake.

A comprehensive guide to advanced econometric identification strategies required for high-stakes public finance research. Covers techniques like Regression Discontinuity Design (RDD), Difference-in-Differences (DiD), Bunching, and Instrumental Variables (IV). It stresses the need to defend policy-induced discontinuities and manage threats of endogeneity, improving the credibility of empirical claims.

This guide establishes rigorous methodological standards for incorporating forecasting and prediction claims in academic manuscripts. It emphasizes the necessity of genuine out-of-sample evaluation, comparison against robust benchmarks (like Random Walk or AR models), and utilizing advanced statistical tests (e.g., Diebold-Mariano) to prove prediction superiority, moving beyond simple in-sample fit reporting.

A detailed guide on establishing and reporting the necessary robustness and sensitivity analyses for manuscripts in quantitative economics and econometrics. It clarifies that the required robustness differs significantly from causal inference studies, emphasizing testing against variations in method choice, parameters, data scope, and sample periods. Provides structured methodologies for generating sensitivity matrices and discussing model stability.

A comprehensive guide on crafting convincing spatial exhibits for top-tier economics journals, especially those focusing on urban economics. This skill helps structure the necessary maps, regression tables, and event-study plots required to visually demonstrate the identifying variation (e.g., treatment boundaries, discontinuities). It focuses on best practices—such as reporting spatial standard errors and making maps do econometric work—rather than running the analysis itself.

A comprehensive guide for developing rigorous spatial economic models, designed for manuscripts needing deep theoretical interpretation (e.g., JUE submissions). It instructs users on how to build quantitative spatial models (QSMs), sorting models, and spatial-equilibrium frameworks to interpret reduced-form estimates, establish counterfactual policies, and ensure that theory rigorously disciplines the empirical findings. Focus areas include general-equilibrium reallocation, policy invariance, and identifying parameters.