Skill UI

This guide provides a rigorous structural and strategic framework for responding to a 'Revise and Resubmit' (R&R) decision, specifically tailored for the Journal of Labor Economics (JOLE). It emphasizes drafting concise, point-by-point rebuttals, addressing complex identification challenges with robust diagnostics, reconciling conflicting referee demands, and strictly adhering to the journal's word economy and single-blind submission rules. It is a critical tool for refining advanced academic manuscripts.

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.

A comprehensive skill for advanced meta-analysis in psychology, designed to assess the reliability and sources of heterogeneity. It performs moderator/subgroup analyses (meta-regression) to explain effect size variation and runs multiple publication-bias diagnostics (e.g., Funnel plot, Egger's test, p-curve, selection models). Essential for rigorous academic reporting requiring robust evidence and sensitivity checks.

This guide provides a structured framework for responding to reviewers and editors regarding a meta-analysis or systematic synthesis. Because such revisions often involve expanding the search, changing eligibility criteria, or adding complex bias diagnostics, the response must maintain impeccable internal statistical consistency across all tables, figures, and estimates. It ensures all methodological changes are clearly propagated and justified.

This guide is designed for systematic reviews and meta-analyses, providing a rigorous framework to stress-test the robustness and completeness of reported findings. It mandates advanced checks such as saturation evidence, risk-of-bias appraisal, and statistical diagnostics (e.g., heterogeneity, publication bias) to ensure conclusions are not artifacts of omission or methodological weakness. Essential for high-level academic research.

A comprehensive guide for executing and stress-testing empirical analyses in Industrial Organization (IO). It covers structural and reduced-form model diagnostics, advanced inference techniques (e.g., wild-cluster bootstrap), counterfactual validation, and meeting the rigorous robustness standards demanded by top-tier academic journals. Focuses on methodology, not study design.

This skill bridges theoretical econometric designs (DiD, IV, RDD, SCM, DML) to concrete, audited statistical estimates. It orchestrates the necessary tool calls to fit models, generate comprehensive diagnostics, and report reliable findings, ensuring that every academic claim is backed by executable, traceable code. Essential for advanced causal inference research.

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.