Skill UI

A comprehensive framework for optimizing inventory levels and planning replenishment in multi-location retail environments. It covers advanced demand forecasting techniques (e.g., Holt-Winters, ML models), calculating safety stock based on variability, estimating promotional uplift, and generating actionable purchase orders while minimizing stockouts and excess inventory. Ideal for professionals managing complex SKU portfolios across various supply chain systems.

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.

This tool allows users to manage and update deal and opportunity records within the CRM pipeline. Use it to move deals between stages, adjust values, add detailed notes, and track overall pipeline velocity. It provides a full visibility into deal progression, calculates downstream financial impacts, and supports both individual and bulk updates, ensuring accurate forecasting for both sales and marketing teams.

Comprehensive guidelines for conducting rigorous empirical validation in financial forecasting and econometrics manuscripts. This framework covers essential steps such as ensuring data provenance, performing robust out-of-sample testing (rolling windows), establishing multi-layered benchmark models, and conducting significance tests (e.g., DM/MCS). It helps authors meet the high academic standards required for top-tier journals.

Provides a systematic guide for authors to assess if their academic manuscript aligns with a specific journal's scope. It helps authors determine the primary contribution—whether it should be framed as quantitative measurement, forecasting, or method application, rather than relying solely on causal inference, thereby guiding optimal revisions and submission routes.

This guide establishes rigorous methodological standards for incorporating forecasting and prediction claims in academic manuscripts. It emphasizes the necessity of genuine out-of-sample evaluation, comparison against robust benchmarks (like Random Walk or AR models), and utilizing advanced statistical tests (e.g., Diebold-Mariano) to prove prediction superiority, moving beyond simple in-sample fit reporting.

This guide provides detailed instructions on writing the policy implications section for manuscripts submitted to quantitative journals (like JQTE). It emphasizes grounding all policy recommendations in the paper's specific quantitative findings (measurement, forecasting, or decomposition results), ensuring the advice is actionable, timely, and relevant to planning, industry, and technology decisions.

This guide details the optimal structure for writing a high-quality literature review, especially for quantitative economics journals. It advises users to avoid chronological descriptions and instead organize the review around the core method lineage (how a quantity is measured, forecasted, or decomposed) and the specific application gap in China's data context. This ensures the literature review logically and powerfully leads to the paper's unique contribution.

A comprehensive workflow guide designed for submitting manuscripts to The Journal of Quantitative & Technological Economics (JQTE). This router determines the optimal sequence of specialized skills (e.g., topic selection, measurement, econometrics, forecasting, rebuttal) based on the manuscript's current stage or contribution type. It helps researchers structure their work to meet the journal's specific requirements, emphasizing measurable methods and reproducibility over pure narrative causality.

This guide assists researchers in selecting the most appropriate and rigorous methodology for Production and Operations Management (POM) manuscripts. It covers various research tracks, including analytical modeling, stochastic process theory, game theory, causal empirical identification (DiD, IV, RD), behavioral experiments, simulation, and operations data science (ML/forecasting). It ensures the research method is tightly linked to the core operational question and possesses significant practical value.

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.

A rigorous framework for making evidence-based estimates and forecasts when precise data is unavailable. It guides users to anchor predictions on base rates, articulate uncertainty using confidence intervals, and systematically update beliefs using likelihood ratios (prior to posterior) as new evidence arrives. Ideal for risk sizing, project timelines, and strategic decision-making.