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Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth 1st ed. 2017 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 94 pages, height x width: 235x155 mm, weight: 454 g, 19 Illustrations, color; 1 Illustrations, black and white; VI, 94 p. 20 illus., 19 illus. in color., 1 Paperback / softback
  • Sērija : SpringerBriefs in Economics
  • Izdošanas datums: 08-Jan-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319690132
  • ISBN-13: 9783319690131
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  • Mīkstie vāki
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Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth 1st ed. 2017Palielināt
  • Formāts: Paperback / softback, 94 pages, height x width: 235x155 mm, weight: 454 g, 19 Illustrations, color; 1 Illustrations, black and white; VI, 94 p. 20 illus., 19 illus. in color., 1 Paperback / softback
  • Sērija : SpringerBriefs in Economics
  • Izdošanas datums: 08-Jan-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319690132
  • ISBN-13: 9783319690131
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319690131.html
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.
1 Why This Book?
1(6)
References
6(1)
2 Data, Variables, and Their Sources
7(12)
2.1 Variables and Their Sources
12(3)
2.2 Problems with Institutional Measures
15(3)
2.3 Imputing Missing Data
18(1)
References
18(1)
3 Methodology
19(10)
3.1 Estimation Techniques
20(6)
3.1.1 Artificial Neural Networks
21(1)
3.1.2 Regression Tree Predictors
22(1)
3.1.3 Boosting Algorithms
23(1)
3.1.4 Bootstrap Aggregating (Bagging) Predictor
24(1)
3.1.5 Random Forests
25(1)
3.2 Predictive Accuracy
26(1)
3.3 Variable Importance and Partial Dependence
27(2)
References
28(1)
4 Predicting a Country's Growth: A First Look
29(8)
References
36(1)
5 Predicting Economic Growth: Which Variables Matter
37(20)
5.1 Evaluating Traditional Variables
40(5)
5.2 Policy Levers
45(12)
References
55(2)
6 Predicting Recessions: What We Learn from Widening the Goalposts
57(18)
6.1 Predictive Quality
58(4)
6.2 Variable Importance and Partial Dependence Plots: What Do We Learn?
62(13)
6.2.1 The First Lens: Implications for Modeling Recessions Theoretically
62(3)
6.2.2 The Second Lens: A Policy Maker and a Data Scientist Walk into a Bar
65(8)
References
73(2)
Epilogue 75(2)
Appendix: R Codes and Notes 77(14)
References 91