Econometric Forecasting Workflow Builder
Build econometric models and generate data-driven forecasts instantly
What You Can Do
This skill helps you construct econometric forecasting models from time series data without writing code. You define variables, specify relationships, and Claude generates ARIMA, VAR, and regression models with diagnostic tests. Get confidence intervals, residual analysis, and publication-ready forecasts in minutes.
Features
Compares ARIMA, VECM, VAR, and OLS specifications to identify the best-fit model for your data based on AIC/BIC criteria and diagnostic tests.
Interprets ACF/PACF plots, performs Augmented Dickey-Fuller and KPSS stationarity tests, and suggests differencing or transformation strategies.
Generates Ljung-Box, Jarque-Bera, and heteroskedasticity tests to validate model assumptions and identify specification issues.
Produces forecast ranges at custom confidence levels (68%, 95%, 99%) and runs alternative scenarios under different parameter assumptions.
Automatically translates regression outputs into economic narrative—elasticities, marginal effects, and policy implications in plain language.
Pre-built templates for common forecasting tasks: demand planning, macro scenarios, supply shocks, and policy simulations.
Exports complete, runnable Python (statsmodels) and R (forecast, vars) code so you can extend models or integrate into pipelines.
Generates markdown tables, LaTeX formulas, and formatted results suitable for reports, presentations, and academic papers.
Example Output
Example 1: Quarterly GDP Forecast
Model Selected: VAR(2) with seasonality adjustment Forecast (95% CI):
| Quarter | GDP Growth | Lower Bound | Upper Bound |
|---|---|---|---|
| Q4 2026 | 2.8% | 2.1% | 3.5% |
| Q1 2027 | 2.5% | 1.5% | 3.5% |
| Q2 2027 | 2.3% | 1.1% | 3.5% |
Diagnostics: Ljung-Box p=0.34 (residuals not autocorrelated) ✓
Example 2: Monthly Demand Forecast
Model: ARIMA(1,1,1) with exogenous price variable Interpretation: 10% price increase → 3.2% demand reduction (elasticity = -0.32) Next 6-month forecast with 80% confidence range provided as table and Python code
What's Included
- Model Selection & Comparison Framework: Step-by-step guidance on choosing between ARIMA, VAR, VECM, and regression models based on your data structure and research question.
- Diagnostic Test Checklist: Templates for stationarity, autocorrelation, heteroskedasticity, and normality tests with interpretation rules for each.
- Forecasting Workflow Generator: Customizable workflows for different scenarios—demand planning, macro forecasting, policy simulation—with embedded decision trees.
- Economic Interpretation Guide: Rules and templates for translating coefficients, elasticities, and impulse responses into business and policy language.
- Code Export Templates: Ready-to-run Python (statsmodels, sklearn) and R (forecast, vars) code snippets for model fitting, validation, and forecasting.
- Visualization & Reporting Templates: Markdown tables, Plotly/ggplot2 code, and LaTeX formulas for time series plots, ACF/PACF, forecast confidence bands, and residuals.
Who It's For
- Economists and Econometricians
- Data Scientists and Quantitative Analysts
- Financial Forecasters and Risk Analysts
- Policy Researchers and Government Analysts
- Supply Chain and Demand Planners
Best For
- Time series forecasting and trend analysis
- Economic impact assessments and scenario modeling
- Demand, sales, and revenue forecasting
- Macroeconomic and policy simulations
- Model diagnostics and specification testing
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