Machine Learning for Econometrics

Machine Learning for Econometrics

Architect the predictive frameworks of the next era and master the high-performance protocols of modern causal inference. Machine Learning for Econometrics provides a definitive, engineering-first roadmap to the most significant shift in statistical modeling since the digital revolution. Learn how to move beyond standard linear regressions to high-velocity, high-dimensional discovery—bridging the gap between a standalone data point and a sophisticated, policy-relevant ecosystem—ensuring your analytical infrastructures are resilient, scalable, and ready for the 2026 global economic landscape.

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What You Will Learn:

Foundations of Algorithmic Econometrics: Master the core principles of Lasso, Random Forests, and the essential mechanics of cross-validation in a causal context.

Modern High-Dimensional Analysis: Step-by-step guidance on utilizing double machine learning and debiased methods with professional-grade precision.

Non-Parametric Structural Estimation: Discover how to utilize deep learning for structural modeling to maintain the technical agility of your economic forecasts.

Strategic Security & Model Integrity: Learn advanced techniques for maintaining information security within your datasets, protecting proprietary economic indicators and ensuring the ethical handling of sensitive financial records.

Who This Book is For: This professional-grade guide is essential for Applied Economists, Data Scientists, and Financial Engineers. It is an invaluable resource for any technical lead—including those building highly secure, data-driven graduation projects like Smart Guard—aiming to master the structural integrity and technical agility required for modern, evidence-based software delivery.

Product Details:

Format: Digital PDF Download

Authors: Christophe Gaillac; Jérémy L'Hour

Publisher: Oxford University Press

ISBN-13: 9780198918820

ISBN-10: 0198918828