Econometrics with machine learning:
Gespeichert in:
| Weitere Verfasser: | , |
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| Format: | Buch |
| Sprache: | English |
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Cham
Springer
[2022]
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| Schriftenreihe: | Advanced studies in theoretical and applied econometrics
Volume 53 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxii, 371 Seiten Illustrationen |
| ISBN: | 9783031151484 |
Internformat
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| 245 | 1 | 0 | |a Econometrics with machine learning |c Felix Chan, László Mátyás, editors |
| 264 | 1 | |a Cham |b Springer |c [2022] | |
| 300 | |a xxii, 371 Seiten |b Illustrationen | ||
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| 490 | 1 | |a Advanced studies in theoretical and applied econometrics |v Volume 53 | |
| 653 | 0 | |a Econometrics. | |
| 653 | 0 | |a Machine learning. | |
| 653 | 0 | |a Macroeconomics. | |
| 653 | 0 | |a Machine Learning and causality | |
| 653 | 0 | |a Linear models | |
| 653 | 0 | |a Non-linear models | |
| 653 | 0 | |a Econometric forecasting and prediction | |
| 653 | 0 | |a Policy evaluation | |
| 653 | 0 | |a Network data | |
| 653 | 0 | |a Poverty | |
| 653 | 0 | |a Inequality | |
| 653 | 0 | |a Machine learning in Finance | |
| 653 | 0 | |a Empirical applications | |
| 653 | 0 | |a Testing statistical hypotheses | |
| 653 | 0 | |a Big data | |
| 653 | 0 | |a Econometric techniques | |
| 653 | 0 | |a Modelling macroeconomic relations | |
| 653 | 0 | |a Discrete Choice models | |
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 700 | 1 | |a Chan, Felix |0 (DE-588)1243770287 |4 edt | |
| 700 | 1 | |a Mátyás, László |d 1957- |0 (DE-588)131764632 |4 edt | |
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Datensatz im Suchindex
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| adam_text | Contents 1 2 Linear Econometrie Models with Machine Learning............................ Felix Chan and Làszló Mátyás 1.1 Introduction........................................................................................... 1.2 Shrinkage Estimators and Regularizers ............................................. 1.2.1 L7 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........................................................................................................ 1 1 3 6 7 10 11 13 13 14 15 16 18 20 22 23 25 27 28 30 31 33 34 34 37 Nonlinear Econometric Models with Machine Learning...................... 41 Felix Chan, Mark N. Harris, Ranjodh B. Singh and Wei (Ben) Ern Yeo 2.1 Introduction.......................................................................................... 42 2.2 Regularization for Nonlinear Econometric Models.......................... 43 XV
xvi Contents 2,2.1 Regularization with Nonlinear Least Squares ................... 44 2,2.2 Regularization with Likelihood Function........................... 46 Continuous Response Variable........................................................................ 47 Discrete Response Variables............................................................................ 48 2.2.3 Estimation, Tuning Parameter and Asymptotic Properties 50 Estimation.......................................................................................................... 50 Tuning Parameter and Cross-Validation.......................................................... 51 Asymptotic Properties and Statistical Inference........................................... 52 2.2.4 Monte Carlo Experiments - Binary Modelwith shrinkage 56 2.2.5 Applications to Econometrics............................................... 61 2.3 Overview of Tree-based Methods - Classification Trees and Random Forest.......................................................................... 63 2.3.1 Conceptual Example of a Tree............................................... 66 2.3.2 Bagging and Random Forests ............................................... 68 2.3.3 Applications and Connections to Econometrics.................. 70 Inference ........................................................................................................... 73 2.4 Concluding Remarks........................................................................... 75
Appendix........................................................................................................... 76 Proof of Proposition 2.1................................................................................... 76 Proof of Proposition 2.2................................................................................... 76 References......................................................................................................... 76 3 The Use of Machine Learning in Treatment Effect Estimation........... 79 Robert P. Lieli, Yu-Chin Hsu and Ágoston Reguly 3.1 Introduction............................................................................................ 79 3.2 The Role of Machine Learning in Treatment Effect Estimation: a Selection-on-Observables Setup......................................................... 82 3.3 Using Machine Learning to Estimate Average Treatment Effects .. 84 3.3.1 Direct versus Double Machine Learning ........................... 84 3.3.2 Why Does Double Machine Learning Work and Direct Machine Learning Does Not? ............................. 87 3.3.3 DML in a Method of Moments Framework........................ 89 3.3.4 Extensions and Recent Developments in DML................. 90 3.4 Using Machine Learning to Discover Treatment Effect Heterogeneity 92 3.4.1 The Problem of Estimatingthe CATE Function ............... 92 3.4.2 The Causal Tree Approach.................................................. 94 3.4.3 Extensions and Technical Variations on the Causal Tree
Approach............................................................... 98 3.4.4 The Dimension Reduction Approach................................ 99 3.5 Empirical Illustration............................................................................. 101 3.6 Conclusion............................................................................................... 105 References.......................................................................................................... 106
Contents xvii 4 Forecasting with Machine Learning Methods........................................ Ill Marcelo C. Medeiros 4.1 Introduction............................................................................................. Ill 4.1.1 Notation.................................................................................. 113 4.1.2 Organization............................................................................ 113 4.2 Modeling Framework and ForecastConstruction................................ 113 4.2.1 Setup........................................................................................ 114 4.2.2 Forecasting Equation.............................................................. 114 4.2.3 Backtesting.............................................................................. 115 4.2.4 Model Choice and Estimation.............................................. 117 4.3 Forecast Evaluation and Model Comparison....................................... 120 4.3.1 The Diebold-Mariano Test.....................................................121 4.3.2 Li-Liao-Quaedvlieg Test........................................................ 122 4.3.3 Model Confidence Sets.......................................................... 124 4.4 Linear Models........................................................................................ 125 4.4.1 Factor Regression.................................................................. 125 4.4.2 Bridging Sparse and Dense Models .....................................127 4.4.3 Ensemble
Methods................................................................ 128 4.5 Nonlinear Models................................................................................... 131 4.5.1 Feedforward Neural Networks............................................... 131 4.5.2 Long Short Term Memory Networks ...................................136 4.5.3 Convolution Neural Networks.............................................. 139 4.5.4 Autoenconders: Nonlinear Factor Regression ..................... 145 4.5.5 Hybrid Models........................................................................ 145 4.6 Concluding Remarks ............................................................................ 146 References......................................................................................................... 147 5 Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods ............................. 151 William Crown 5.1 Introduction........................................................................................... 152 5.2 Naïve Estimation of Causal Effects in Outcomes Models with Binary Treatment Variables..................................................... 152 5.3 Is Machine Learning Compatible with Causal Inference?.............. 154 5.4 The Potential Outcomes Model........................................................... 155 5.5 Modeling the Treatment Exposure Mechanism-Propensity Score Matching and Inverse Probability Treatment Weights .......... 157 5.6 Modeling Outcomes and Exposures: Doubly Robust Methods
.... 158 5.7 Targeted Maximum Likelihood Estimation (TMLE) for Causal Inference................................................................................... 160 5.8 Empirical Applications of TMLE in Health Outcomes Studies .... 163 5.8.1 Use of Machine Learning to Estimate TMLE Models.... 163 5.9 Extending TMLE to Incorporate Instrumental Variables................ 164 5.10 Some Practical Considerations on the Use of IVs.............................. 165 5.11 Alternative Definitions of Treatment Effects..................................... 166
xviii Contents 5.12 A Final Word on the Importance of Study Design in Mitigating Bias 168 References........................................................................................................... 169 Econometrics of Networks with Machine Learning............................... 177 Oliver Kiss and Gyorgy Ruzicska 6.1 Introduction.............................................................................................. 177 6.2 Structure, Representation, and Characteristics of Networks............. 179 6.3 The Challenges of Working with Network Data..................................182 6.4 Graph Dimensionality Reduction.......................................................... 185 6.4.1 Types of Embeddings.............................................................. 186 6.4.2 Algorithmic Foundations of Embeddings.............................. 187 6.5 Sampling Networks ................................................................................ 189 6.5.1 Node Sampling Approaches.................................................... 190 6.5.2 Edge Sampling Approaches.................................................... 191 6.5.3 Traversal-Based Sampling Approaches.................................. 192 6.6 Applications of Machine Learning in the Econometrics of Networks 196 6.6.1 Applications of Machine Learning in Spatial Models .... 196 6.6.2 Gravity Models for Flow Prediction.................................... 203 6.6.3 The Geographically Weighted Regression Model and ML 205 6.7 Concluding Remarks
............................................................................ 209 References.......................................................................................................... 210 6 7 8 Fairness in Machine Learning and Econometrics................................... 217 Samuele Centorrino, Jean-Pierre Florens and Jean-Michel Loubes 7.1 Introduction............................................................................................. 218 7.2 Examples in Econometrics..................................................................... 222 7.2.1 Linear IV Model.................................................................... 222 7.2.2 A Nonlinear IV Model with Binary Sensitive Attribute .. 223 7.2.3 Fairness and Structural Econometrics.................................. 223 7.3 Fairness for Inverse Problems............................................................... 224 7.4 Full Fairness IV Approximation............................................................227 7.4.1 Projection onto Fairness........................................................ 228 7.4.2 Fair Solution of the Structural IV Equation.........................230 7.4.3 Approximate Fairness............................................................ 234 7.5 Estimation with an Exogenous Binary Sensitive Attribute................. 240 7.6 An Illustration......................................................................................... 243 7.7 Conclusions.............................................................................................. 247
References......................................................................................................... 248 Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance........................................... 251 Ekaterina Seregina 8.1 Introduction............................................................................................. 251 8.1.1 Notation..................................................................................252 8.2 Graphical Models:Methodology and Existing Approaches................ 253 8.2.1 GraphicalLASSO.................................................................. 255
Contents xix 8.2.2 Nodewise Regression............................................................ 258 8.2.3 CLIME.................................................................................... 259 8.2.4 Solution Techniques.............................................................. 260 8.3 Graphical Models in the Context of Finance..................................... 262 8.3.1 The No-Short-Sale Constraint and Shrinkage .....................267 8.3.2 The Λ-Norm Constraint and Shrinkage................................ 270 8.3.3 Classical Graphical Models for Finance.............................. 272 8.3.4 Augmented Graphical Models for FinanceApplications . 273 8.4 Graphical Models in the Context of Economics ............................... 278 8.4.1 Forecast Combinations.......................................................... 278 8.4.2 Vector Autoregressive Models.............................................. 280 8.5 Further Integration of Graphical Models with Machine Learning.. 283 References........................................................................................................ 285 9 Poverty, Inequality and Development Studies with Machine Learning 291 Walter Sosa-Escudero, Maria Victoria Anauati and Wendy Brau 9.1 Introduction............................................................................................ 291 9.2 Measurement and Forecasting.............................................................. 293 9.2.1 Combining Sources to Improve Data Availability............. 294 9.2.2 More Granular
Measurements............................................. 298 9.2.3 Dimensionality Reduction ................................................... 304 9.2.4 Data Imputation...................................................................... 306 9.2.5 Methods.................................................................................. 307 9.3 Causal Inference.................................................................................... 307 9.3.1 Heterogeneous Treatment Effects ........................................ 307 9.3.2 Optimal Treatment Assignment............................................ 312 9.3.3 Handling High-Dimensional Data and Debiased ML .... 313 9.3.4 Machine-Building Counterfactuals...................................... 315 9.3.5 New Data Sources for Outcomes and Treatments............. 316 9.3.6 Combining Observational and Experimental Data ............. 319 9.4 Computing Power and Tools................................................................ 320 9.5 Concluding Remarks............................................................................ 322 References........................................................................................................ 325 10 Machine Learning for Asset Pricing....................................................... 337 Jantje Sönksen 10.1 Introduction.......................................................................................... 337 10.2 How Machine Learning Techniques Can Help Identify Stochastic Discount Factors.......................................................................
343 10.3 How Machine Learning Techniques Can Test/Evaluate Asset Pricing Models......................................................................... 345 10.4 How Machine Learning Techniques Can Estimate Linear Factor Models....................................................................................... 348 10.4.1 Gagliardi™, Ossola, and Scaillet’s (2016) Econometric Two-Pass Approach for Assessing Linear Factor Models . 349
Contents XX Kelly, Pruitt, and Su’s (2019) Instrumented Principal Components Analysis.............................................. 350 10.4.3 Gu, Kelly, and Xiu’s (2021) Autoencoder..............................351 10.4.4 Kozak, Nagel, and Santosh’s (2020)Regularized Bayesian Approach................................................................. 352 10.4.5 Which Factors to Choose and How to Deal with Weak Factors? .................................................................... 352 10.5 How Machine Learning Can Predict in Empirical Asset Pricing... 356 10.6 Concluding Remarks............................................................................. 359 Appendix 1 : An Upper Bound for the Sharpe Ratio...................................... 359 Appendix 2: A Comparison of Different PCA Approaches.......................... 360 References........................................................................................................... 361 10.4.2 Appendix.....................................................................................................................367 A Terminology.......................................................................................................367 A. 1 Introduction.............................................................................................367 A.2 Terms...................................................................................................... 367
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| adam_txt |
Contents 1 2 Linear Econometrie Models with Machine Learning. Felix Chan and Làszló Mátyás 1.1 Introduction. 1.2 Shrinkage Estimators and Regularizers . 1.2.1 L7 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. 1 1 3 6 7 10 11 13 13 14 15 16 18 20 22 23 25 27 28 30 31 33 34 34 37 Nonlinear Econometric Models with Machine Learning. 41 Felix Chan, Mark N. Harris, Ranjodh B. Singh and Wei (Ben) Ern Yeo 2.1 Introduction. 42 2.2 Regularization for Nonlinear Econometric Models. 43 XV
xvi Contents 2,2.1 Regularization with Nonlinear Least Squares . 44 2,2.2 Regularization with Likelihood Function. 46 Continuous Response Variable. 47 Discrete Response Variables. 48 2.2.3 Estimation, Tuning Parameter and Asymptotic Properties 50 Estimation. 50 Tuning Parameter and Cross-Validation. 51 Asymptotic Properties and Statistical Inference. 52 2.2.4 Monte Carlo Experiments - Binary Modelwith shrinkage 56 2.2.5 Applications to Econometrics. 61 2.3 Overview of Tree-based Methods - Classification Trees and Random Forest. 63 2.3.1 Conceptual Example of a Tree. 66 2.3.2 Bagging and Random Forests . 68 2.3.3 Applications and Connections to Econometrics. 70 Inference . 73 2.4 Concluding Remarks. 75
Appendix. 76 Proof of Proposition 2.1. 76 Proof of Proposition 2.2. 76 References. 76 3 The Use of Machine Learning in Treatment Effect Estimation. 79 Robert P. Lieli, Yu-Chin Hsu and Ágoston Reguly 3.1 Introduction. 79 3.2 The Role of Machine Learning in Treatment Effect Estimation: a Selection-on-Observables Setup. 82 3.3 Using Machine Learning to Estimate Average Treatment Effects . 84 3.3.1 Direct versus Double Machine Learning . 84 3.3.2 Why Does Double Machine Learning Work and Direct Machine Learning Does Not? . 87 3.3.3 DML in a Method of Moments Framework. 89 3.3.4 Extensions and Recent Developments in DML. 90 3.4 Using Machine Learning to Discover Treatment Effect Heterogeneity 92 3.4.1 The Problem of Estimatingthe CATE Function . 92 3.4.2 The Causal Tree Approach. 94 3.4.3 Extensions and Technical Variations on the Causal Tree
Approach. 98 3.4.4 The Dimension Reduction Approach. 99 3.5 Empirical Illustration. 101 3.6 Conclusion. 105 References. 106
Contents xvii 4 Forecasting with Machine Learning Methods. Ill Marcelo C. Medeiros 4.1 Introduction. Ill 4.1.1 Notation. 113 4.1.2 Organization. 113 4.2 Modeling Framework and ForecastConstruction. 113 4.2.1 Setup. 114 4.2.2 Forecasting Equation. 114 4.2.3 Backtesting. 115 4.2.4 Model Choice and Estimation. 117 4.3 Forecast Evaluation and Model Comparison. 120 4.3.1 The Diebold-Mariano Test.121 4.3.2 Li-Liao-Quaedvlieg Test. 122 4.3.3 Model Confidence Sets. 124 4.4 Linear Models. 125 4.4.1 Factor Regression. 125 4.4.2 Bridging Sparse and Dense Models .127 4.4.3 Ensemble
Methods. 128 4.5 Nonlinear Models. 131 4.5.1 Feedforward Neural Networks. 131 4.5.2 Long Short Term Memory Networks .136 4.5.3 Convolution Neural Networks. 139 4.5.4 Autoenconders: Nonlinear Factor Regression . 145 4.5.5 Hybrid Models. 145 4.6 Concluding Remarks . 146 References. 147 5 Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods . 151 William Crown 5.1 Introduction. 152 5.2 Naïve Estimation of Causal Effects in Outcomes Models with Binary Treatment Variables. 152 5.3 Is Machine Learning Compatible with Causal Inference?. 154 5.4 The Potential Outcomes Model. 155 5.5 Modeling the Treatment Exposure Mechanism-Propensity Score Matching and Inverse Probability Treatment Weights . 157 5.6 Modeling Outcomes and Exposures: Doubly Robust Methods
. 158 5.7 Targeted Maximum Likelihood Estimation (TMLE) for Causal Inference. 160 5.8 Empirical Applications of TMLE in Health Outcomes Studies . 163 5.8.1 Use of Machine Learning to Estimate TMLE Models. 163 5.9 Extending TMLE to Incorporate Instrumental Variables. 164 5.10 Some Practical Considerations on the Use of IVs. 165 5.11 Alternative Definitions of Treatment Effects. 166
xviii Contents 5.12 A Final Word on the Importance of Study Design in Mitigating Bias 168 References. 169 Econometrics of Networks with Machine Learning. 177 Oliver Kiss and Gyorgy Ruzicska 6.1 Introduction. 177 6.2 Structure, Representation, and Characteristics of Networks. 179 6.3 The Challenges of Working with Network Data.182 6.4 Graph Dimensionality Reduction. 185 6.4.1 Types of Embeddings. 186 6.4.2 Algorithmic Foundations of Embeddings. 187 6.5 Sampling Networks . 189 6.5.1 Node Sampling Approaches. 190 6.5.2 Edge Sampling Approaches. 191 6.5.3 Traversal-Based Sampling Approaches. 192 6.6 Applications of Machine Learning in the Econometrics of Networks 196 6.6.1 Applications of Machine Learning in Spatial Models . 196 6.6.2 Gravity Models for Flow Prediction. 203 6.6.3 The Geographically Weighted Regression Model and ML 205 6.7 Concluding Remarks
. 209 References. 210 6 7 8 Fairness in Machine Learning and Econometrics. 217 Samuele Centorrino, Jean-Pierre Florens and Jean-Michel Loubes 7.1 Introduction. 218 7.2 Examples in Econometrics. 222 7.2.1 Linear IV Model. 222 7.2.2 A Nonlinear IV Model with Binary Sensitive Attribute . 223 7.2.3 Fairness and Structural Econometrics. 223 7.3 Fairness for Inverse Problems. 224 7.4 Full Fairness IV Approximation.227 7.4.1 Projection onto Fairness. 228 7.4.2 Fair Solution of the Structural IV Equation.230 7.4.3 Approximate Fairness. 234 7.5 Estimation with an Exogenous Binary Sensitive Attribute. 240 7.6 An Illustration. 243 7.7 Conclusions. 247
References. 248 Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance. 251 Ekaterina Seregina 8.1 Introduction. 251 8.1.1 Notation.252 8.2 Graphical Models:Methodology and Existing Approaches. 253 8.2.1 GraphicalLASSO. 255
Contents xix 8.2.2 Nodewise Regression. 258 8.2.3 CLIME. 259 8.2.4 Solution Techniques. 260 8.3 Graphical Models in the Context of Finance. 262 8.3.1 The No-Short-Sale Constraint and Shrinkage .267 8.3.2 The Λ-Norm Constraint and Shrinkage. 270 8.3.3 Classical Graphical Models for Finance. 272 8.3.4 Augmented Graphical Models for FinanceApplications . 273 8.4 Graphical Models in the Context of Economics . 278 8.4.1 Forecast Combinations. 278 8.4.2 Vector Autoregressive Models. 280 8.5 Further Integration of Graphical Models with Machine Learning. 283 References. 285 9 Poverty, Inequality and Development Studies with Machine Learning 291 Walter Sosa-Escudero, Maria Victoria Anauati and Wendy Brau 9.1 Introduction. 291 9.2 Measurement and Forecasting. 293 9.2.1 Combining Sources to Improve Data Availability. 294 9.2.2 More Granular
Measurements. 298 9.2.3 Dimensionality Reduction . 304 9.2.4 Data Imputation. 306 9.2.5 Methods. 307 9.3 Causal Inference. 307 9.3.1 Heterogeneous Treatment Effects . 307 9.3.2 Optimal Treatment Assignment. 312 9.3.3 Handling High-Dimensional Data and Debiased ML . 313 9.3.4 Machine-Building Counterfactuals. 315 9.3.5 New Data Sources for Outcomes and Treatments. 316 9.3.6 Combining Observational and Experimental Data . 319 9.4 Computing Power and Tools. 320 9.5 Concluding Remarks. 322 References. 325 10 Machine Learning for Asset Pricing. 337 Jantje Sönksen 10.1 Introduction. 337 10.2 How Machine Learning Techniques Can Help Identify Stochastic Discount Factors.
343 10.3 How Machine Learning Techniques Can Test/Evaluate Asset Pricing Models. 345 10.4 How Machine Learning Techniques Can Estimate Linear Factor Models. 348 10.4.1 Gagliardi™, Ossola, and Scaillet’s (2016) Econometric Two-Pass Approach for Assessing Linear Factor Models . 349
Contents XX Kelly, Pruitt, and Su’s (2019) Instrumented Principal Components Analysis. 350 10.4.3 Gu, Kelly, and Xiu’s (2021) Autoencoder.351 10.4.4 Kozak, Nagel, and Santosh’s (2020)Regularized Bayesian Approach. 352 10.4.5 Which Factors to Choose and How to Deal with Weak Factors? . 352 10.5 How Machine Learning Can Predict in Empirical Asset Pricing. 356 10.6 Concluding Remarks. 359 Appendix 1 : An Upper Bound for the Sharpe Ratio. 359 Appendix 2: A Comparison of Different PCA Approaches. 360 References. 361 10.4.2 Appendix.367 A Terminology.367 A. 1 Introduction.367 A.2 Terms. 367 |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV048492202 |
| illustrated | Illustrated |
| index_date | 2024-07-03T20:42:12Z |
| indexdate | 2024-07-10T09:39:35Z |
| institution | BVB |
| isbn | 9783031151484 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033869648 |
| oclc_num | 1352877384 |
| open_access_boolean | |
| owner | DE-11 DE-355 DE-BY-UBR DE-N2 |
| owner_facet | DE-11 DE-355 DE-BY-UBR DE-N2 |
| physical | xxii, 371 Seiten Illustrationen |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer |
| record_format | marc |
| series | Advanced studies in theoretical and applied econometrics |
| series2 | Advanced studies in theoretical and applied econometrics |
| spelling | Econometrics with machine learning Felix Chan, László Mátyás, editors Cham Springer [2022] xxii, 371 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Advanced studies in theoretical and applied econometrics Volume 53 Econometrics. Machine learning. Macroeconomics. Machine Learning and causality Linear models Non-linear models Econometric forecasting and prediction Policy evaluation Network data Poverty Inequality Machine learning in Finance Empirical applications Testing statistical hypotheses Big data Econometric techniques Modelling macroeconomic relations Discrete Choice models (DE-588)4143413-4 Aufsatzsammlung gnd-content Chan, Felix (DE-588)1243770287 edt Mátyás, László 1957- (DE-588)131764632 edt Erscheint auch als Online-Ausgabe 978-3-031-15149-1 Advanced studies in theoretical and applied econometrics Volume 53 (DE-604)BV000002376 53 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033869648&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Econometrics with machine learning Advanced studies in theoretical and applied econometrics |
| subject_GND | (DE-588)4143413-4 |
| title | Econometrics with machine learning |
| title_auth | Econometrics with machine learning |
| title_exact_search | Econometrics with machine learning |
| title_exact_search_txtP | Econometrics with machine learning |
| title_full | Econometrics with machine learning Felix Chan, László Mátyás, editors |
| title_fullStr | Econometrics with machine learning Felix Chan, László Mátyás, editors |
| title_full_unstemmed | Econometrics with machine learning Felix Chan, László Mátyás, editors |
| title_short | Econometrics with machine learning |
| title_sort | econometrics with machine learning |
| topic_facet | Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033869648&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000002376 |
| work_keys_str_mv | AT chanfelix econometricswithmachinelearning AT matyaslaszlo econometricswithmachinelearning |