Skill UI

This guide provides a rigorous framework for stress-testing and implementing state-of-the-art causal identification strategies required for high-impact empirical economics. It covers advanced methodologies including Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Synthetic Control, and Structural Modeling. It emphasizes modern estimators (e.g., Callaway-Sant'Anna, effective F) and robust diagnostic checks to ensure the causal claims are methodologically sound and defensible.

A comprehensive guide for establishing credible causal inference in accounting and finance research. This methodology covers advanced techniques including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RDD), and experimental design. It instructs users on how to move beyond simple controls, defining the specific identifying variation necessary for publishing rigorous, causality-focused academic work.

A comprehensive methodological guide detailing high standards for causal inference in finance research, specifically tailored for top journals like RFS. It covers advanced techniques such as Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and asset-pricing identification, ensuring designs meet rigorous standards for exogenous variation and endogeneity control.

A comprehensive guide for constructing non-empirical, theoretical figures for sociological theory manuscripts. It details how to design conceptual exhibits—including mechanism diagrams, process models, typologies, and concept maps—that carry theoretical work. Crucially, it emphasizes that these figures must illustrate mechanisms or classifications, and must never include raw data, regression tables, or empirical results.

A comprehensive methodological guide for designing rigorous academic studies in accounting. It details advanced econometric techniques—including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and experimental setups—to establish credible causal identification. Use this guide when your research challenge is solving endogeneity or isolating a specific economic channel from complex, non-random variables.

This skill details the rigorous process of converting raw statistical outputs into self-contained, publication-ready tables and figures for top-tier academic journals. It ensures compliance with strict house styles, covering sample selection, descriptive statistics, main regression results, and specific formatting for DiD, RDD, and event studies, all while adhering to strict page limits and providing economic magnitude interpretation.

Scikit-learn is the industry-standard Python library for classical machine learning. It provides comprehensive tools for supervised and unsupervised learning tasks, including classification, regression, clustering, and dimensionality reduction. Use it to build robust, production-ready ML pipelines, perform rigorous model evaluation, and preprocess complex datasets for various data science applications.

Statsmodels is Python's premier library for rigorous statistical modeling. It provides comprehensive tools for estimation, inference, and diagnostics across a wide range of statistical methods. Use this skill for advanced analysis, including linear regression (OLS, WLS), generalized linear models (Logistic, Poisson), and complex time series forecasting (ARIMA). Ideal for econometric studies and producing publication-ready results.