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

<p><strong>Sergey Svetunkov</strong>, 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.</p>

<p><strong>Ivan Svetunkov </strong>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”.</p>

<p>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.</p>

<p>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.</p>

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