Skill UI

This comprehensive skill guides authors on assessing the suitability and framing of manuscripts targeting the Journal of Risk and Uncertainty (JRU). It covers rigorous criteria including decision-theoretic grounding, methodological rigor, risk valuation techniques, and required structure, ensuring submissions meet the journal's high standards for advancing choice models under risk.

This guide helps structure the conceptual framework (Theory of Change or Logic Model) for policy research. It moves beyond simple correlation by explicitly mapping the causal chain: identifying the policy lever, the underlying mechanism, and the predicted outcome. It is crucial for establishing external validity and defining scope conditions so that empirical findings can be generalized and actionable by policymakers in different settings.

A comprehensive guide to strengthening research designs for public administration manuscripts, particularly for rigorous journals that emphasize experimental and causal identification. It covers experimental designs, causal observational methods (DID, IV, RDD), multilevel modeling, and mixed methods, providing frameworks to defend against common methodological biases and rival explanations.

This tool is used when an economic research paper requires explicit theoretical modeling or a clear causal mechanism to explain empirical results. It focuses on building and disciplining the underlying economic argument, ensuring that empirical findings are connected to fundamental economic forces, general equilibrium, and rigorous theoretical predictions. It helps transform descriptive findings into testable, theoretically sound research.

This guide provides a comprehensive overview of the editorial and review process for the Journal of Public Economics (JPubE). It details the single anonymized review model, expectations regarding multiple specialist reviewers, the use of Editorial Manager, and strategic advice for SSRN preprints and appeals. Use this skill when planning a submission, understanding review outcomes, or managing academic publication expectations.

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 rigorous methodological framework for identifying core risk and uncertainty parameters in academic research. This guide details how to design experiments or structural models to explicitly separate confounds (e.g., utility curvature vs. probability weighting, preferences vs. beliefs) and ensure that the measured parameter is genuinely identified, thereby enhancing the scientific rigor of behavioral finance and econometrics studies.

A comprehensive guide for researchers developing models in decision theory under risk and uncertainty. It outlines how to strengthen theoretical representations by ensuring the model is anchored by clear axiomatic foundations, a mathematically tractable functional form, and crucially, verifiable behavioral content (i.e., what the model predicts or forbids). This helps bridge the gap between abstract theory and empirical data.

This guide provides rigorous structural and stylistic guidelines for academic manuscripts in the field of risk and uncertainty. It instructs authors on how to precisely articulate decision models, quantify results with appropriate magnitude and precision, and structure arguments (Abstract, Introduction, Exposition) to meet the high standards of specialized journals, ensuring technical honesty and clarity.

A comprehensive guide for advanced empirical research in open economies. It outlines best practices for establishing causal inference by anchoring identification strategies in external or institutional shocks (e.g., tariffs, trade agreements, exchange rate reforms). The guide emphasizes modern critiques of standard methods like shift-share and gravity models, providing detailed protocols for DID, Bartik decomposition, and IV estimation to meet top-tier academic journal standards.

This guide provides best practices for ensuring the scientific rigor and reproducibility of data mining and machine learning research published at top-tier conferences like KDD. It details a three-tier honesty model (Rerunnable, Rebuildable, Attested) and provides comprehensive checklists for disclosing pipeline stages, handling stochasticity, and documenting configurations to build reader trust.