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Complex-Valued Econometrics with Examples in R

Autor Sergey Svetunkov, Ivan Svetunkov
en Limba Engleză Hardback – 26 iul 2024
This book explores the application of complex variables to econometric modeling. Providing a thorough introduction to the theory of complex numbers, it extends these concepts to develop complex-valued models that enhance the accuracy and depth of economic forecasting and data analysis. From simple to multiple complex linear regression, the monograph discusses model formulation, estimation techniques, and correlation analysis, supported by examples in R.
This comprehensive guide is a useful resource for students, researchers, and practitioners aiming to apply advanced mathematical techniques to tackle complex real-life problems, making it a useful tool for enhancing predictive analytics in business, economics, and finance.
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Specificații

ISBN-13: 9783031626074
ISBN-10: 3031626079
Pagini: 168
Ilustrații: X, 120 p.
Dimensiuni: 160 x 241 x 15 mm
Greutate: 0.46 kg
Ediția:2024
Editura: Springer
Locul publicării:Cham, Switzerland

Cuprins

Chapter 1. Introduction to theory of complex variables.- Chapter 2. Simple Complex Linear Regression.- Chapter 3. Correlation analysis of complex random variables.- Chapter 4. Multiple Complex Linear Regression.- Chapter 5. Assumptions of Complex Linear Models.- Chapter 6. Complex Dynamic Models.- Chapter 7. Examples of application.

Notă biografică

Sergey Svetunkov, PhD in Economics, Doctor of Economic Sciences, Professor at the Peter the Great St. Petersburg Polytechnic University, is the leading expert in the field of mathematical modelling in economics and economic forecasting. He is an author of more than 250 scientific publications. Over the last few decades, he has also acted as an expert of the Russian Science Foundation.
Ivan Svetunkov is a Lecturer of Marketing Analytics at Lancaster University, UK. He has PhD in Management Science from Lancaster University and a candidate degree in economics from Saint Petersburg State University of Economics and Finance. His main area of interest is statistical learning for forecasting, focusing on demand forecasting in healthcare, supply chains and retail. He is a creator and a maintainer of several forecasting- and analytics-related R packages and an author of many papers and a monograph “Forecasting and Analytics with the Augmented Dynamic Adaptive Model”.

Textul de pe ultima copertă

This book explores the application of complex variables to econometric modeling. Providing a thorough introduction to the theory of complex numbers, it extends these concepts to develop complex-valued models that enhance the accuracy and depth of economic forecasting and data analysis. From simple to multiple complex linear regression, the monograph discusses model formulation, estimation techniques, and correlation analysis, supported by examples in R.
This comprehensive guide is a useful resource for students, researchers, and practitioners aiming to apply advanced mathematical techniques to tackle complex real-life problems, making it a useful tool for enhancing predictive analytics in business, economics, and finance.

Caracteristici

Offers an original approach to complex-valued autoregressions Presents new sections of mathematical statistics of a complex random variable Useful for the practice of modeling complex stochastic processes, including multidimensional processes