Data Science for Financial Econometrics (Hardcover, 1st ed. 2021)

This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 898
Release date:	November 2020
First published:	2021
Editors:	Nguyen Ngoc Thach • Vladik Kreinovich • Nguyen Duc Trung
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	633
Edition:	1st ed. 2021
ISBN-13:	978-3-03-048852-9
Categories:	Books > Business & Economics > Economics > Economic theory & philosophy Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-048852-7
Barcode:	9783030488529

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