Skill UI

This guide provides a rigorous framework for strengthening economic manuscripts submitted to top journals, such as AEJ: Micro. It details how to systematically test the robustness of main results through theoretical extensions (e.g., relaxing assumptions, perturbations) and applied/experimental methods (e.g., sensitivity analyses, placebo tests). Learn how to identify extensions that truly 'earn their place' and significantly enhance the overall academic contribution of your work.

This guide outlines best practices for authors presenting theoretical and empirical results for top-tier journals like the American Economic Journal: Microeconomics (AEJ: Micro). It covers structuring propositions, building transparent numerical examples, creating schematic figures, and formatting tables (including mandated use of standard errors over significance asterisks) to ensure maximum clarity and adherence to academic publishing standards.

This skill provides a rigorous framework for constructing high-quality microeconomic theory papers, adhering to the standards of top academic journals like AEJ: Micro. It guides users through defining minimal, motivated model primitives (players, actions, payoffs), selecting and justifying the correct equilibrium concept (e.g., Nash, SPBE), and choosing a robust proof architecture (e.g., duality, fixed-point). It ensures the resulting theorems are general, illuminating, and deeply motivated by economic principles.

This guide helps researchers determine if their microeconomic theory, model, or experiment is suitable for American Economic Journal: Microeconomics (AEJ: Micro). It provides a structured framework for evaluating the contribution's scope, assessing broad microeconomic interest, and differentiating the appropriate venue from competing specialist journals like JET, Econometrica, or AEJ: Applied.

Provides comprehensive guidelines for structuring theoretical economics manuscripts for submission to AEJ: Micro. It enforces the theory-first approach, ensuring the introduction immediately presents the economic question and states the main result in words, thus maximizing impact for a broad micro audience. It also covers specific AEA house style conventions, such as reporting standard errors and structuring the abstract.

A comprehensive framework for translating reduced-form causal estimates (e.g., elasticity, treatment effects) into explicit policy statements. It guides researchers on building clear welfare, cost-benefit, or optimal-policy models, defining sufficient statistics, and articulating distributional effects, ensuring policy claims are rigorously grounded in empirical data.

A comprehensive guide for building and strengthening the conceptual or theoretical framework for academic manuscripts, particularly for submission to top-tier journals like AERJ. It teaches researchers how to ensure that theory drives the entire study—from question generation and design choices to data analysis and interpretation—transforming findings from mere reports into genuine theoretical contributions.

This skill guides authors in assessing the suitability of their research concerning agricultural or forest ecosystems for journals focused on land–atmosphere interactions. It emphasizes that the core contribution must be a biophysical process—such as the exchange of energy, water, or carbon—rather than purely agronomic or ecological observations. Use it to re-frame your work to highlight micrometeorological processes and robust flux measurement methodologies.

A comprehensive guide designed to help researchers select, articulate, and defend the appropriate interpretive lens and scale for historical arguments. Whether focusing on social, cultural, global, or microhistory, this skill assists in structuring a robust methodology, ensuring that theory is always grounded in primary evidence, and preventing common academic pitfalls like anachronism or over-generalization.

This guide provides strict guidelines for designing, auditing, and reporting empirical or simulation experiments, particularly for theoretical machine learning and statistics papers. It emphasizes mapping every empirical claim to specific evidence, ensuring statistical rigor (e.g., reporting uncertainty, baselines, seeds), and validating theoretical assumptions rather than optimizing for leaderboards. Essential for research reproducibility.

This comprehensive guide helps researchers position their academic paper for AISTATS, ensuring maximum novelty and eligibility. It provides detailed advice on comparative analysis, teaching authors how to contrast statistical methods against both modern ML literature (e.g., NeurIPS, ICML) and established statistical theory (e.g., JASA, Annals of Statistics). It covers proper citation of concurrent work, understanding archival status, and structuring the 'Related Work' section to highlight unique technical contributions.

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.