MARC details
| 000 -LEADER |
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| fixed length control field |
03153nam a22003857a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IIITD |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250716174033.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250710b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783031151484 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
IIITD |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
330.015 |
| Item number |
CHA-E |
| 245 ## - TITLE STATEMENT |
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| Title |
Econometrics with machine learning |
| Statement of responsibility, etc |
edited by Felix Chan and Laszlo Matyas |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Switzerland : |
| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
©2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxii, 371 p. : |
| Other physical details |
col. ill. ; |
| Dimensions |
24 cm. |
| 490 ## - SERIES STATEMENT |
|---|
| Series statement |
Advanced studies in theoretical and applied econometrics |
| Volume number/sequential designation |
53 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Chapter 1. Linear Econometric Models with Machine Learning |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Chapter 2. Nonlinear Econometric Models with Machine Learning |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Chapter 3. The Use of Machine Learning in Treatment Effect Estimation |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Chapter 4. Forecasting with Machine Learning Methods |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Chapter 5. Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Chapter 6. Econometrics of Networks with Machine Learning |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Chapter 7. Fairness in Machine Learning and Econometrics |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Chapter 8. Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Chapter 9. Poverty, Inequality and Development Studies with Machine Learning |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Chapter 10. Machine Learning for Asset Pricing |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Econometrics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine Learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Quantitative Economics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Econometrics -- Data processing |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Chan, Felix |
| Relator term |
editor |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Matyas, Laszlo |
| Relator term |
editor |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
