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دسته بندی: اقتصاد سنجی ویرایش: نویسندگان: Felix Chan. László Mátyás سری: Advanced Studies in Theoretical and Applied Econometrics, 53 ISBN (شابک) : 3031151488, 9783031151484 ناشر: Springer سال نشر: 2022 تعداد صفحات: 385 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 9 مگابایت
در صورت تبدیل فایل کتاب Econometrics with Machine Learning به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب اقتصاد سنجی با یادگیری ماشینی نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Foreword Preface Acknowledgements Contents List of Contributors Chapter 1 Linear Econometric Models with Machine Learning 1.1 Introduction 1.2 Shrinkage Estimators and Regularizers 1.2.1 ???????? norm, Bridge, LASSO and Ridge 1.2.2 Elastic Net and SCAD 1.2.3 Adaptive LASSO 1.2.4 Group LASSO 1.3 Estimation 1.3.1 Computation and Least Angular Regression 1.3.2 Cross Validation and Tuning Parameters 1.4 Asymptotic Properties of Shrinkage Estimators 1.4.1 Oracle Properties 1.4.2 Asymptotic Distributions 1.4.3 Partially Penalized (Regularized) Estimator 1.5 Monte Carlo Experiments 1.5.1 Inference on Unpenalized Parameters 1.5.2 Variable Transformations and Selection Consistency 1.6 Econometrics Applications 1.6.1 Distributed Lag Models 1.6.2 Panel Data Models 1.6.3 Structural Breaks 1.7 Concluding Remarks Appendix Proof of Proposition 1.1 References Chapter 2 Nonlinear Econometric Models with Machine Learning 2.1 Introduction 2.2 Regularization for Nonlinear Econometric Models 2.2.1 Regularization with Nonlinear Least Squares 2.2.2 Regularization with Likelihood Function Continuous Response Variable Discrete Response Variables 2.2.3 Estimation, Tuning Parameter and Asymptotic Properties Estimation Tuning Parameter and Cross-Validation Asymptotic Properties and Statistical Inference 2.2.4 Monte Carlo Experiments – Binary Model with shrinkage 2.2.5 Applications to Econometrics 2.3 Overview of Tree-based Methods - Classification Trees and Random Forest 2.3.1 Conceptual Example of a Tree 2.3.2 Bagging and Random Forests 2.3.3 Applications and Connections to Econometrics Inference 2.4 Concluding Remarks Appendix Proof of Proposition 2.1 Proof of Proposition 2.2 References Chapter 3 The Use of Machine Learning in Treatment Effect Estimation 3.1 Introduction 3.2 The Role of Machine Learning in Treatment Effect Estimation: a Selection-on-Observables Setup 3.3 Using Machine Learning to Estimate Average Treatment Effects 3.3.1 Direct versus Double Machine Learning 3.3.2 Why Does Double Machine Learning Work and Direct Machine Learning Does Not? 3.3.3 DML in a Method of Moments Framework 3.3.4 Extensions and Recent Developments in DML 3.4 Using Machine Learning to Discover Treatment Effect Heterogeneity 3.4.1 The Problem of Estimating the CATE Function 3.4.2 The Causal Tree Approach 3.4.3 Extensions and Technical Variations on the Causal Tree Approach 3.4.4 The Dimension Reduction Approach 3.5 Empirical Illustration 3.6 Conclusion References Chapter 4 Forecasting with Machine Learning Methods 4.1 Introduction 4.1.1 Notation 4.1.2 Organization 4.2 Modeling Framework and Forecast Construction 4.2.1 Setup 4.2.2 Forecasting Equation 4.2.3 Backtesting 4.2.4 Model Choice and Estimation 4.3 Forecast Evaluation and Model Comparison 4.3.1 The Diebold-Mariano Test 4.3.2 Li-Liao-Quaedvlieg Test 4.3.3 Model Confidence Sets 4.4 Linear Models 4.4.1 Factor Regression 4.4.2 Bridging Sparse and Dense Models 4.4.3 Ensemble Methods 4.4.3.1 Bagging 4.4.3.2 Complete Subset Regression 4.5 Nonlinear Models 4.5.1 Feedforward Neural Networks 4.5.1.1 Shallow Neural Networks 4.5.1.2 Deep Neural Networks 4.5.2 Long Short Term Memory Networks 4.5.3 Convolution Neural Networks 4.5.4 Autoenconders: Nonlinear Factor Regression 4.5.5 Hybrid Models 4.6 Concluding Remarks References Chapter 5 Causal Estimation of Treatment Effects From Obervational Health Care Data Using Machine Learning Methods 5.1 Introduction 5.2 Naïve Estimation of Causal Effects in Outcomes Models with Binary Treatment Variables 5.3 Is Machine Learning Compatible with Causal Inference? 5.4 The Potential Outcomes Model 5.5 Modeling the Treatment Exposure Mechanism–Propensity Score Matching and Inverse Probability Treatment Weights 5.6 Modeling Outcomes and Exposures: Doubly Robust Methods 5.7 Targeted Maximum Likelihood Estimation (TMLE) for Causal Inference 5.8 Empirical Applications of TMLE in Health Outcomes Studies 5.8.1 Use of Machine Learning to Estimate TMLE Models 5.9 Extending TMLE to Incorporate Instrumental Variables 5.10 Some Practical Considerations on the Use of IVs 5.11 Alternative Definitions of Treatment Effects 5.12 A Final Word on the Importance of Study Design in Mitigating Bias References Chapter 6 Econometrics of Networks with Machine Learning 6.1 Introduction 6.2 Structure, Representation, and Characteristics of Networks 6.3 The Challenges of Working with Network Data 6.4 Graph Dimensionality Reduction 6.4.1 Types of Embeddings 6.4.2 Algorithmic Foundations of Embeddings 6.5 Sampling Networks 6.5.1 Node Sampling Approaches 6.5.2 Edge Sampling Approaches Hybrid Approaches and the Importance of the Problem 6.5.3 Traversal-Based Sampling Approaches 6.5.3.1 Search Based Techniques Pseudo Code for Search-Based Sampling Algorithms. 6.5.3.2 RandomWalk-Based Techniques 6.6 Applications of Machine Learning in the Econometrics of Networks 6.6.1 Applications of Machine Learning in Spatial Models 6.6.2 Gravity Models for Flow Prediction 6.6.3 The Geographically Weighted Regression Model and ML 6.7 Concluding Remarks References Chapter 7 Fairness in Machine Learning and Econometrics 7.1 Introduction 7.2 Examples in Econometrics 7.2.1 Linear IV Model 7.2.2 A Nonlinear IV Model with Binary Sensitive Attribute 7.2.3 Fairness and Structural Econometrics 7.3 Fairness for Inverse Problems 7.4 Full Fairness IV Approximation 7.4.1 Projection onto Fairness 7.4.2 Fair Solution of the Structural IV Equation 7.4.3 Approximate Fairness 7.5 Estimation with an Exogenous Binary Sensitive Attribute 7.6 An Illustration 7.7 Conclusions References Chapter 8 Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance 8.1 Introduction 8.1.1 Notation 8.2 Graphical Models: Methodology and Existing Approaches 8.2.1 Graphical LASSO 8.2.2 Nodewise Regression 8.2.3 CLIME 8.2.4 Solution Techniques 8.3 Graphical Models in the Context of Finance 8.3.1 The No-Short-Sale Constraint and Shrinkage 8.3.2 The A-Norm Constraint and Shrinkage 8.3.3 Classical Graphical Models for Finance 8.3.4 Augmented Graphical Models for Finance Applications 8.4 Graphical Models in the Context of Economics 8.4.1 Forecast Combinations 8.4.2 Vector Autoregressive Models 8.5 Further Integration of Graphical Models with Machine Learning References Chapter 9 Poverty, Inequality and Development Studies with Machine Learning 9.1 Introduction 9.2 Measurement and Forecasting 9.2.1 Combining Sources to Improve Data Availability 9.2.2 More Granular Measurements 9.2.2.1 Data Visualization and High-Resolution Maps 9.2.2.2 Interpolation 9.2.2.3 Extended Regional Coverage 9.2.2.4 Extrapolation 9.2.3 Dimensionality Reduction 9.2.4 Data Imputation 9.2.5 Methods 9.3 Causal Inference 9.3.1 Heterogeneous Treatment Effects 9.3.2 Optimal Treatment Assignment 9.3.3 Handling High-Dimensional Data and Debiased ML 9.3.4 Machine-Building Counterfactuals 9.3.5 New Data Sources for Outcomes and Treatments 9.3.6 Combining Observational and Experimental Data 9.4 Computing Power and Tools 9.5 Concluding Remarks References Chapter 10 Machine Learning for Asset Pricing 10.1 Introduction 10.2 How Machine Learning Techniques Can Help Identify Stochastic Discount Factors 10.3 How Machine Learning Techniques Can Test/Evaluate Asset Pricing Models 10.4 How Machine Learning Techniques Can Estimate Linear Factor Models 10.4.1 Gagliardini, Ossola, and Scaillet’s (2016) Econometric Two-Pass Approach for Assessing Linear Factor Models 10.4.2 Kelly, Pruitt, and Su’s (2019) Instrumented Principal Components Analysis 10.4.3 Gu, Kelly, and Xiu’s (2021) Autoencoder 10.4.4 Kozak, Nagel, and Santosh’s (2020) Regularized Bayesian Approach 10.4.5 Which Factors to Choose and How to Deal withWeak Factors? 10.5 How Machine Learning Can Predict in Empirical Asset Pricing 10.6 Concluding Remarks Appendix 1: An Upper Bound for the Sharpe Ratio Appendix 2: A Comparison of Different PCA Approaches References Appendix A Terminology A.1 Introduction A.2 Terms