Reactive Publishing
Real-Time Macroeconomic Nowcasting and Forecasting with Python delivers a practical, hands-on guide to building sophisticated nowcasting and forecasting systems using modern Python tools and techniques.
In today's data-rich environment, traditional quarterly GDP reports and monthly indicators are often too slow for decision-making. This book shows you how to leverage high-frequency data, mixed-frequency models, and machine learning methods to generate timely, accurate macroeconomic insights in real time.
What You'll Learn:
- How to acquire, clean, and align high-frequency economic data (financial markets, alternative data, and official statistics)
- Mixed-frequency modeling techniques including MIDAS, U-MIDAS, and dynamic factor models
- Real-time nowcasting frameworks for GDP, inflation, employment, and other key indicators
- Machine learning approaches for macroeconomic forecasting, including tree-based models, neural networks, and ensemble methods
- Feature engineering strategies specifically designed for economic time series
- Model evaluation, backtesting, and deployment considerations for production environments
- Best practices for handling revisions, ragged-edge data, and publication lags
Written for economists, data scientists, quantitative analysts, and Python developers working in finance, central banking, policy research, or investment, this book bridges the gap between economic theory and practical implementation.
All code examples are built using accessible, open-source Python libraries such as pandas, statsmodels, scikit-learn, TensorFlow/Keras, and specialized time-series packages. Full working examples and best practices are provided so you can move from theory to working models efficiently.
Whether you're looking to enhance your nowcasting capabilities or build production-grade forecasting systems, this book provides the technical foundation and practical guidance needed to work effectively with real-time macroeconomic data.
