Machine Learning for Structural Econometrics With Python: A Hands-On Guide to Lasso, Boosting, and Deep IV for Credible Structural Inference (Richman Computational Economics) - Softcover
Book 27 of 29: Richman Computational Economics
Synopsis
The definitive, hands-on path to modern structural econometrics
Built for economists who need results, this book fuses rigor and implementation to deliver structural identification with state-of-the-art machine learning. Across 24 laser-focused chapters, you’ll move from orthogonal moments and cross-fitting to Lasso instrument selection, boosting for conditional moments, and full-blown neural approaches like Deep IV and deep GMM—then stress-test everything with MCQs and end-to-end Python code.
No fluff. No filler. Just the theory you need, followed by immediate self-checks and production-quality implementations for credible, policy-relevant counterfactuals.
Why this book stands out- Focused and practical: 24 dense chapters each designed to get you from theory to working code fast.
- Inference-first: Orthogonal scores, debiased ML, cross-fitting, and weak-instrument robustness are baked into every workflow.
- Structural credibility: Shape restrictions, moment inequalities, dynamic choices, auctions, platforms, and demand estimation done with ML the right way.
- End-to-end thinking: From identification and tuning to diagnostics, stability checks, and reproducible pipelines.
- Lasso and post-lasso for instrument and control selection, double selection, and partialling out in high dimensions.
- Boosting for first-stage estimation and overidentified moment systems, with early-stopping as regularization.
- Deep IV and control functions with flexible conditional density estimation (mixture density nets, flows).
- Deep GMM and adversarial moments for conditional moment restrictions.
- Panels and time series with regularization (VAR-lasso, factor-lasso), HAC/cluster-robust inference, and dynamic endogeneity.
- Shape-restricted ML (monotonicity, convexity, homogeneity) for demand systems and game-theoretic models.
- Policy learning and counterfactual evaluation with orthogonal value estimators and robust off-policy tools.
- Graduate students and researchers in economics, public policy, finance, and marketing.
- Quantitative analysts and data scientists moving from prediction to causal and structural analysis.
- Practitioners building decision systems that must withstand scrutiny, replication, and policy stakes.
Get the playbook economists use to deliver credible counterfactuals with modern ML.
"synopsis" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. The definitive, hands-on path to modern structural econometricsBuilt for economists who need results, this book fuses rigor and implementation to deliver structural identification with state-of-the-art machine learning. Across 24 laser-focused chapters, you'll move from orthogonal moments and cross-fitting to Lasso instrument selection, boosting for conditional moments, and full-blown neural approaches like Deep IV and deep GMM-then stress-test everything with MCQs and end-to-end Python code.No fluff. No filler. Just the theory you need, followed by immediate self-checks and production-quality implementations for credible, policy-relevant counterfactuals.Why this book stands outFocused and practical: 24 dense chapters each designed to get you from theory to working code fast.Inference-first: Orthogonal scores, debiased ML, cross-fitting, and weak-instrument robustness are baked into every workflow.Structural credibility: Shape restrictions, moment inequalities, dynamic choices, auctions, platforms, and demand estimation done with ML the right way.End-to-end thinking: From identification and tuning to diagnostics, stability checks, and reproducible pipelines.What you'll masterLasso and post-lasso for instrument and control selection, double selection, and partialling out in high dimensions.Boosting for first-stage estimation and overidentified moment systems, with early-stopping as regularization.Deep IV and control functions with flexible conditional density estimation (mixture density nets, flows).Deep GMM and adversarial moments for conditional moment restrictions.Panels and time series with regularization (VAR-lasso, factor-lasso), HAC/cluster-robust inference, and dynamic endogeneity.Shape-restricted ML (monotonicity, convexity, homogeneity) for demand systems and game-theoretic models.Policy learning and counterfactual evaluation with orthogonal value estimators and robust off-policy tools.Who this is forGraduate students and researchers in economics, public policy, finance, and marketing.Quantitative analysts and data scientists moving from prediction to causal and structural analysis.Practitioners building decision systems that must withstand scrutiny, replication, and policy stakes. Get the playbook economists use to deliver credible counterfactuals with modern ML. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798264517730
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Paperback. Condition: new. Paperback. The definitive, hands-on path to modern structural econometricsBuilt for economists who need results, this book fuses rigor and implementation to deliver structural identification with state-of-the-art machine learning. Across 24 laser-focused chapters, you'll move from orthogonal moments and cross-fitting to Lasso instrument selection, boosting for conditional moments, and full-blown neural approaches like Deep IV and deep GMM-then stress-test everything with MCQs and end-to-end Python code.No fluff. No filler. Just the theory you need, followed by immediate self-checks and production-quality implementations for credible, policy-relevant counterfactuals.Why this book stands outFocused and practical: 24 dense chapters each designed to get you from theory to working code fast.Inference-first: Orthogonal scores, debiased ML, cross-fitting, and weak-instrument robustness are baked into every workflow.Structural credibility: Shape restrictions, moment inequalities, dynamic choices, auctions, platforms, and demand estimation done with ML the right way.End-to-end thinking: From identification and tuning to diagnostics, stability checks, and reproducible pipelines.What you'll masterLasso and post-lasso for instrument and control selection, double selection, and partialling out in high dimensions.Boosting for first-stage estimation and overidentified moment systems, with early-stopping as regularization.Deep IV and control functions with flexible conditional density estimation (mixture density nets, flows).Deep GMM and adversarial moments for conditional moment restrictions.Panels and time series with regularization (VAR-lasso, factor-lasso), HAC/cluster-robust inference, and dynamic endogeneity.Shape-restricted ML (monotonicity, convexity, homogeneity) for demand systems and game-theoretic models.Policy learning and counterfactual evaluation with orthogonal value estimators and robust off-policy tools.Who this is forGraduate students and researchers in economics, public policy, finance, and marketing.Quantitative analysts and data scientists moving from prediction to causal and structural analysis.Practitioners building decision systems that must withstand scrutiny, replication, and policy stakes. Get the playbook economists use to deliver credible counterfactuals with modern ML. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798264517730
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