Skill UI

Analyzes options chain data by separating real calls and puts from lottery-like speculative contracts to produce more reliable put/call sentiment, anomaly detection, and expiry breakdowns for watchlists, sectors, and event-driven names.

Generates sophisticated, editorial-grade data visualizations inspired by major news publications like The New York Times. Ideal for presenting complex data (CSV/JSON) or key conclusions as single frames or animations for video explainers, social media posts, or data reports. Features clean design, specific typography, and staggered reveal animations.

A comprehensive guide for writing and verifying the institutional and accounting standard background section in academic manuscripts. This tool emphasizes the critical need for precision, requiring accurate standard names, official document numbers (文号), effective dates, and applicable scopes. Essential for empirical studies relying on regulatory changes, ensuring the manuscript meets the high standards of authoritative accounting journals.

This comprehensive guide details the methodological requirements for building a robust robustness section in accounting and empirical research manuscripts. It covers four critical pillars: substituting accounting measures, controlling for institutional/governance variables, executing placebo tests, and advanced endogeneity follow-ups. It helps authors ensure their primary findings withstand stringent academic scrutiny.

A comprehensive guide for authors preparing empirical research manuscripts (especially in accounting). It details best practices for structuring main tables (keeping them lean), transforming statistical coefficients into interpretable economic magnitudes, and properly visualizing dynamic effects using event study graphs. It emphasizes moving beyond simple significance stars to provide deep academic context.

This guide provides a rigorous framework for defending causal identification claims in empirical microeconomics, tailored for top-tier journals like AEJ: Applied. It covers modern techniques and anti-patterns for RCTs, Difference-in-Differences (DiD), Regression Discontinuity (RDD), and Instrumental Variables (IV), ensuring the mapping from variation to the causal estimand is explicit, defensible, and falsifiable.

A comprehensive guide for formatting and presenting empirical results (tables and figures) for submission to the American Economic Journal: Applied Economics (AEJ: Applied). It ensures that main causal estimates are immediately legible to referees by adhering to rigorous academic standards, including presenting standard errors, confidence intervals, and specific exhibit structures like event-study plots and balance tables.

This guide provides a methodology for integrating theoretical models into empirical economics papers, especially for journals emphasizing empirical findings (e.g., AEJ: Applied). It teaches researchers how to ensure that theoretical components—such as mechanisms, welfare statements, or counterfactuals—serve to interpret, sharpen, or guide the existing empirical estimates, rather than leading the paper or replacing the core identification provided by the design. Focus on sufficient statistics and minimal model assumptions is key.

This guide outlines the rigorous methodological standards for empirical identification in macroeconomics, crucial for submitting to top-tier journals. It covers advanced techniques such as Structural VAR (SVAR), Local Projections (LP), and handling high-frequency/micro-data shocks. It ensures authors explicitly defend the mapping from raw data to dynamic causal objects, addressing critical issues like endogeneity, anticipation, and structural breaks, and providing execution checklists for modern econometric tools (DiD, IV, RDD).

A comprehensive framework designed to rigorously test the stability of key macroeconomic findings. This program addresses potential biases arising from specification choices, sample periods (e.g., Great Moderation, ZLB, COVID breaks), and methodological variations (SVAR vs. LP). It ensures that the reported headline quantity remains robust even when challenged by a skeptical academic referee, providing a targeted defense rather than simply listing extra tables. Ideal for high-stakes empirical macro papers.

A comprehensive guide for submitting high-quality replication packages for academic journals like the American Economic Journal (AEJ: Micro). This package covers two essential components: self-contained, verifiable proofs for theoretical claims (the Proof Appendix), and a fully reproducible code and data deposit (the Code/Data Repository). It ensures that all numerical results, simulations, and empirical findings can be regenerated by the journal's editor, maintaining the highest standards of scientific transparency.

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.