E-Book, Englisch, 97 Seiten
Reihe: SpringerBriefs in Economics
Basuchoudhary / Bang / Sen Machine-learning Techniques in Economics
1. Auflage 2017
ISBN: 978-3-319-69014-8
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark
New Tools for Predicting Economic Growth
E-Book, Englisch, 97 Seiten
Reihe: SpringerBriefs in Economics
ISBN: 978-3-319-69014-8
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.
Autoren/Hrsg.
Weitere Infos & Material
1;Contents;6
2;Chapter 1: Why This Book?;8
2.1;References;13
3;Chapter 2: Data, Variables, and Their Sources;14
3.1;2.1 Variables and Their Sources;19
3.2;2.2 Problems with Institutional Measures;22
3.3;2.3 Imputing Missing Data;25
3.4;References;25
4;Chapter 3: Methodology;26
4.1;3.1 Estimation Techniques;27
4.1.1;3.1.1 Artificial Neural Networks;28
4.1.2;3.1.2 Regression Tree Predictors;29
4.1.3;3.1.3 Boosting Algorithms;30
4.1.4;3.1.4 Bootstrap Aggregating (Bagging) Predictor;31
4.1.5;3.1.5 Random Forests;32
4.2;3.2 Predictive Accuracy;33
4.3;3.3 Variable Importance and Partial Dependence;34
4.4;References;35
5;Chapter 4: Predicting a Country´s Growth: A First Look;36
5.1;References;43
6;Chapter 5: Predicting Economic Growth: Which Variables Matter;44
6.1;5.1 Evaluating Traditional Variables;47
6.2;5.2 Policy Levers;52
6.3;References;62
7;Chapter 6: Predicting Recessions: What We Learn from Widening the Goalposts;64
7.1;6.1 Predictive Quality;65
7.2;6.2 Variable Importance and Partial Dependence Plots: What Do We Learn?;69
7.2.1;6.2.1 The First Lens: Implications for Modeling Recessions Theoretically;69
7.2.2;6.2.2 The Second Lens: A Policy Maker and a Data Scientist Walk into a Bar;72
7.3;References;80
8;Epilogue;81
9;Appendix: R Codes and Notes;83
9.1;Imputing and Processing the Data;83
9.2;Training the Models;85
9.3;Evaluating Predictive Quality;88
9.4;Variable Importance and Partial Dependence;93
10;References;97
