CPI Multilateral Learning Dashboard

An elegant, interactive Quarto-based learning application for mastering CPI multilateral price index methods.

🎯 Overview

This static website provides a comprehensive 4-week learning path covering fundamental to advanced price index calculation methods, with practical implementations in both R and Python.

📚 Course Content

Week 1: Elementary Indices

• Jevons Index (geometric mean of price relatives)
• Dutot Index (ratio of arithmetic means)
• Carli Index (arithmetic mean of price relatives)
• Mathematical formulations and properties
• R and Python implementations with visualizations

Week 2: Fisher & Törnqvist Indices

• Laspeyres and Paasche indices
• Fisher Index (superlative index)
• Törnqvist Index (weighted geometric mean)
• Axiomatic properties and tests
• Bilateral index theory

Week 3: GEKS & GEKS-T Methods

• GEKS (Gini-Éltető-Köves-Szulc) method
• GEKS-Törnqvist approach
• Transitivity in multilateral comparisons
• Rolling window strategies
• Window length considerations

Week 4: Advanced Techniques

• CCDI (Coordinated Consumer Price Index)
• CPD (Country-Product-Dummy) regression method
• Rolling window implementation
• Linking strategies (movement, window, half splice)
• Extension methods for long time series

🚀 Getting Started

Prerequisites

• Quarto (v1.4+)
• R (v4.0+) with packages: ggplot2, dplyr, tidyverse
• Python (v3.8+) with packages: numpy, pandas, matplotlib, statsmodels, scikit-learn

Building the Site

# Clone the repository
git clone https://github.com/MaryleneH/ipc-multilateral-quarto.git
cd ipc-multilateral-quarto

# Preview the site (with live reload)
quarto preview

# Render the site
quarto render

The rendered site will be in the _site/ directory.

🎨 Design Features

• Elegant UX: Modern dashboard with rounded cards, gradients, and smooth animations
• Soft Blue Palette: Professional color scheme (#2563eb primary, #60a5fa secondary)
• Inter Font: Clean, readable typography
• Bootstrap Icons: Visual indicators throughout
• Responsive Design: Mobile-first approach with breakpoints
• Interactive Elements: Progress bars, KPI cards, skill badges, timeline

📊 Features

• Dashboard: Overview of learning path with progress tracking
• KPI Cards: Visual metrics for weeks completed, exercises, and time estimates
• Skill Badges: Interactive badges for key competencies
• Progress Bars: Visual tracking of weekly completion
• Timeline: Step-by-step learning path visualization
• Code Examples: Working R and Python implementations
• Exercises: Guided practical exercises with hints
• Mathematical Formulas: LaTeX-rendered equations
• Visualizations: ggplot2 and matplotlib charts

📁 Project Structure

ipc-multilateral-quarto/
├── _quarto.yml          # Quarto configuration
├── custom.scss          # Custom SCSS theme
├── styles.css           # Main stylesheet
├── index.qmd            # Dashboard page
├── week1.qmd            # Week 1 content
├── week2.qmd            # Week 2 content
├── week3.qmd            # Week 3 content
├── week4.qmd            # Week 4 content
└── _site/               # Generated site (gitignored)

🎓 Learning Outcomes

By completing this course, you will:

• ✅ Understand elementary price indices and their properties
• ✅ Implement bilateral superlative indices (Fisher, Törnqvist)
• ✅ Master multilateral comparison methods (GEKS, GEKS-T)
• ✅ Apply advanced techniques (CCDI, CPD, linking)
• ✅ Code price indices in R and Python
• ✅ Visualize price index data effectively
• ✅ Handle real-world implementation challenges

📖 References

• ILO, IMF, OECD, Eurostat, UN, World Bank (2020). Consumer Price Index Manual: Concepts and Methods
• Diewert, W.E. (1976). "Exact and Superlative Index Numbers"
• Hill, R.J. (2004). "Constructing Price Indexes Across Space and Time"
• Ivancic, L., Diewert, W.E., & Fox, K.J. (2011). "Scanner Data, Time Aggregation and the Construction of Price Indexes"

🤝 Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

📄 License

This project is open source and available for educational purposes.

🌟 Acknowledgments

Built with Quarto, designed following Lovable UX principles.