Skill UI

Automates time series decomposition work in data analytics by guiding through patterns and best practices, generating production-ready code/configuration, and validating outputs for SQL, visualization, and BI workflows.

A powerful Python library for analyzing, validating, converting, and transforming complex materials structures and associated computational data. It provides functionalities for handling compositions, periodic crystals, molecules, local phase diagrams, symmetry assignments, and electronic structure I/O, ensuring strict provenance tracking for all transformations.

This comprehensive guide details the rigorous standards for conducting and reporting advanced demographic analyses. It covers essential techniques, including constructing accurate rates, performing decomposition, generating life tables, and handling event history/survival data. The core focus is on methodological rigor, ensuring the reproducibility of results, and honestly reporting uncertainty (e.g., confidence and credible intervals) to meet the standards of expert peer review.

This guide assists users in structuring and rigorously defending the methodological design for demographic manuscripts. It provides detailed guidance on selecting and justifying advanced techniques—including life tables, decomposition analysis, survival models, Age-Period-Cohort (APC) modeling, and microsimulation. It emphasizes critical steps like establishing identification assumptions, addressing causal biases (selection and exposure), and formulating an adjudication test to rule out rival explanations, ensuring the research is methodologically robust.

This guide teaches how to restructure complex econometric tables and figures for manuscripts intended for a dual audience of professional economists and non-technical policymakers. The focus is on clarity, interpretability, and impact, ensuring that the main text presents policy-relevant findings (e.g., event studies, welfare decomposition) while relegating dense technical details (full regression dumps, diagnostics) to the appendix. It emphasizes translating standard coefficients into meaningful units like jobs or % of GDP.

A comprehensive guide for advanced econometric techniques in causal inference, covering modern approaches for quasi-experimental designs such as staggered Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Double Machine Learning (DML). It details best practices, critical diagnostic tests (e.g., weak instruments, parallel trend checks, bias decomposition), and robust reporting standards required for high-impact economic research.

This comprehensive guide outlines the high standards and scope required for publishing in Food Chemistry. It emphasizes rigorous analytical chemistry, detailed method validation (LOD/LOQ, precision), quantified compositional results, and strong chemical mechanism interpretation. Use this tool to assess whether your manuscript meets the necessary scientific rigor for this specialized journal.

A comprehensive guide for conducting reproducible empirical analysis in labor economics, adhering to high academic standards (e.g., JOLE norms). It covers essential steps including sample construction from CPS/ACS/registers, advanced wage decompositions (Oaxaca/RIF), proper handling of standard errors, and robust sensitivity checks to ensure results are fully replicable.

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.

This guide provides essential knowledge for selecting and defending data sources for advanced research in international economics. It emphasizes the critical need for data to possess a 'global attribute,' covering trade flows, Global Value Chains (GVC), and cross-country comparisons. Key data types include micro-level customs data (firm-product-destination), cross-country panels (WDI, IMF), and global input-output tables (WIOD/ADB MRIO) for GVC decomposition.

A comprehensive guide for advanced empirical research in open economies. It outlines best practices for establishing causal inference by anchoring identification strategies in external or institutional shocks (e.g., tariffs, trade agreements, exchange rate reforms). The guide emphasizes modern critiques of standard methods like shift-share and gravity models, providing detailed protocols for DID, Bartik decomposition, and IV estimation to meet top-tier academic journal standards.

This comprehensive guide details professional standards for presenting complex empirical economic results (tables and figures) for top-tier journals like The Journal of World Economy. It instructs users on simplifying main regression tables, standardizing notes (clustering, significance), and correctly presenting specialized open-economy objects such as event studies, gravity regressions, and GVC decompositions, ensuring research meets publication quality.