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.

A comprehensive skill designed for academics submitting papers in urban, regional, or spatial economics. It guides authors through evaluating their manuscript's suitability for the Journal of Urban Economics (JUE). The tool emphasizes the necessity of a credible spatial mechanism, robust identification methods (e.g., DiD, RDD), and adherence to high-level empirical and structural modeling standards required by top-tier journals.

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 provides a rigorous framework for advanced econometric modeling in quantitative economics. It covers time series, panel data, cointegration, and mixed-frequency techniques (VAR, VECM, GMM, MIDAS). It emphasizes proper model diagnostics, stationarity testing, and grounding models in specific data properties and economic contexts.

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.

Provides comprehensive guidelines for building and validating complex structural economic models, including CGE, IO, and Structural Decomposition. It emphasizes mandatory elements like explicit data sourcing, parameter calibration, macroeconomic closure rules, and scenario reproducibility to ensure models are not 'black boxes' and are scientifically rigorous.

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.