Skill UI

This skill performs mean-variance portfolio optimization by solving the linear system Σ · x = μ using the Conjugate Gradient (CG) method. It is significantly faster (40-60x) and numerically robust compared to legacy methods like the Neumann series. It takes a covariance matrix (Σ) and expected return vector (μ) to determine optimal asset weights, providing full provenance tracking for auditability.

A comprehensive tool for training and evaluating advanced neural network models (including LSTM, Transformer, and N-BEATS) on time-series market data. It facilitates the prediction of financial trends, generating results with confidence intervals, and comparing performance across multiple architectures. Ideal for quantitative trading research and financial forecasting.

A comprehensive guide for executing and reporting robust statistical analyses for manuscripts in Global Change Biology (GCB). This skill ensures the analysis meets the high standards of reproducibility required for publicly archived data (DOI). It covers advanced methods including mixed/hierarchical modeling, time-series and spatial analysis, meta-analysis, and rigorous uncertainty quantification (parameter, structural, and scenario). It emphasizes statistical rigor over result fabrication, guiding the use of appropriate inferential machinery based on data structure.

This guide details the rigorous, reproducible empirical strategies required to identify causal parameters in applied econometrics, meeting high standards (e.g., JAE). It covers various designs—including DID, IV, panel data, and time-series—and mandates that every assumption and diagnostic test must be explicitly stated, tested, and reproducible using deposited code/data.

A comprehensive guide for constructing and validating cross-country and time-series panel data in international macro-finance. It addresses common pitfalls in measurement choices (e.g., gross vs. net flows, USD vs. NEER), sample period justification (e.g., GFC, ZLB), and frequency mismatch, ensuring methodological rigor required by top-tier journals.

A comprehensive guide to constructing rigorous empirical datasets for academic papers in finance and banking. It covers best practices for handling complex data sources (e.g., bank micro-data, central bank reports, macro series), addressing measurement biases, and ensuring full replicability, adhering to the standards of top-tier economic journals. Focus areas include merger tracking, regime transitions, and variable definition consistency.

This guide outlines the rigorous formal requirements for econometric papers, focusing on achieving the level of mathematical rigor expected by top academic journals. It covers proving identification, verifying primitive assumptions, constructing asymptotic derivations (consistency, rate, distribution), and structuring proofs for maximum verifiability in causal inference and time series analysis.

This tool helps authors assess if their research manuscript is suitable for the Journal of Business and Economic Statistics (JBES). It evaluates the fit across methodological rigor, economic relevance, time series analysis, and microeconometrics, providing a comprehensive self-check against the journal's high standards and suggesting alternative venues.

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.

This specialized skill is designed for creating investor pitch decks for various funding rounds (Seed, Series A/B/C). It builds upon standard presentation rules, adding critical VC-focused constraints such as stage-appropriate metrics, the canonical narrative arc (Problem -> Solution -> Market -> Ask), and finance-grade number conventions. Use this only when actively raising capital, not for general reviews.

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.