Applied Econometric Forecasting with Python : Causal Models, Time Series, and Predictive Analytics

Hayden van der Post

ISBN 13: 9798245339887

Edité par Independently Published Jan 2026, 2026

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Neuware.

N° de réf. du vendeur 9798245339887

Détails bibliographiques

• Titre: Applied Econometric Forecasting with Python : Causal Models, Time Series, and Predictive Analytics
• Éditeur: Independently Published Jan 2026
• Date de parution: 2026
• Langue: anglais
• ISBN à 13 chiffres: 9798245339887
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 229 L x 152 H x 34 P (en millimètres) 229 millimètres de largeur par 152 millimètres de hauteur par 34 millimètres de profondeur
• Poids: 636 grammes

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Synopsis

Reactive Publishing

Modern forecasting is no longer about guessing the future. It is about engineering it. Economists, analysts, and quantitative leaders now demand models that explain why, not just what. This book shows how to build causal and predictive forecasting systems using the full power of econometrics and Python, bridging classical statistical tools with machine learning, structural modeling, and real-world business applications.

Readers learn how to design time series pipelines, estimate causal effects, and translate empirical models into operational forecasts that drive executive decisions. From ARIMA to VAR, from causal inference to Bayesian time series, and from model selection to forecast evaluation, the book provides a rigorous yet accessible framework for forecasting markets, macroeconomic indicators, commodities, operational demand, financial performance, and policy scenarios.

Beyond the theory, Applied Econometric Forecasting with Python emphasizes implementation. Full workflows demonstrate how to structure data, choose the correct econometric formulation, evaluate forecast accuracy, and deploy models at scale. The book closes with advanced chapters on structural breaks, adaptive forecasting, rolling horizons, scenario analysis, and machine learning augmentation.

You will learn:
• How to construct causal models that isolate drivers and explain economic behavior
• How to implement econometric time series forecasting pipelines in Python
• How to integrate machine learning with classical econometrics for more robust predictions
• How to evaluate forecast performance and uncertainty
• How to build rolling, scenario-based, and probabilistic forecasts
• How to translate empirical models into operational decision frameworks

Ideal for:
Finance professionals, economists, data scientists, policy analysts, enterprise planning teams, academic researchers, and quantitative practitioners seeking a rigorous applied forecasting playbook.

The future belongs to those who can quantify uncertainty, measure causality, and model change. This book shows you how.