VUZF UNIVERSITY OF FINANCE, BUSINESS AND ENTREPRENEURSHIP Olha Mezentseva Oksana Ilienko Oleksii Kolesnikov Olena Savielieva Dmytro Lukianov FORECASTING MODELING AND ANALYTICS OF ECONOMIC PROCESSES Sofia, 2020 1 VUZF University of Finance, Business and Entrepreneurship Publishing House “St. Grigorii Bogoslov” Chief Editor: Assoc. Prof. Dr. Grigorii Vazov Deputy Chief Editor: Assoc. Prof. Dr. Radostin Vazov Members: Prof. Doctor of Economic Sciences Metodi Hristov, Prof. Dr. Radoslaw Grabowski, Prof. Dr. Daniela Bobeva, Prof. Dr. Emilia Milanova, Prof. Dr. Virginia Zhelyazkova, Prof. Dr. Grigor Dimitrov, Assoc. Prof. Dr. Yakim Kitanov, Assoc. Prof. Dr. Desislava Yosifova, Assoc. Prof. Dr. Stanislav Dimitrov, Assoc. Prof. Dr. Krassimir Todorov, Assoc. Prof. Dr Daniela Ilieva Recommended for publication by the Editorial Board of the VUZF University of Finance, Business and Entrepreneurship, 12 August 2020 Reviewers: Igor Britchenko – Doctor of Economics, Professor, Department of Finance, VUZF University of Finance, Business and Entrepreneurship, Sofia, Bulgaria Maksym Bezpartochnyi – Doctor of Economics, Professor at the Department of Economics and Marketing, National Aerospace University named after N. Zhukovsky “Kharkiv Aviation Institute”, Kharkiv, Ukraine; Professor at the University Economics and Social, Przeworsk, Poland Jelena Caiko – Doctor of Technical Sciences, Professor, ISMA University, Riga, Latvia D 94 Forecasting modeling and analytics of economic processes / O. Mezentseva, O. Ilienko, O. Kolesnikov, O. Savielieva, D. Lukianov. Sofia: VUZF Publishing House “St. Grigorii Bogoslov”, 2020. – 252 p. The book will be useful for economists, finance and valuation professionals, market researchers, public policy analysts, data analysts, teachers or students in graduate-level classes. The book is aimed at students and beginners who are interested in forecasting modeling and analytics of economic processes and want to get an idea of its implementation. © O.Mezentseva, O.Ilienko, O.Kolesnikov, O.Savielieva, D.Lukianov, 2020 © VUZF Publishing House “St. Grigorii Bogoslov”, 2020 ISBN 978-954-8590-88-4 2 TABLE OF CONTESTS Introduction ............................................................................ 5 CHAPTER 1. Mathematical and Simulation Models in Business Economics .................................................................... 9 CHAPTER 2. Library Functions for Business Economics ..................................................................................... 15 2.1 Model Demand Equation .................................................... 22 CHAPTER 3. Economic Impact Models ............................ 29 CHAPTER 4. Fiscal Impact Models .................................. 51 CHAPTER 5. Applications for Business ........................ 61 5.1 Business Valuation and Damages Estimation ................... 64 5.2 The Role of Economist and Accountant in Business Valuation ......................................................................................... 68 CHAPTER 6. Applications for Finance ........................ 102 6.1 Auto Depreciation .............................................................. 108 CHAPTER 7. Modeling Location and Retail Sales . 114 7.1 Applying Economic Theory to Sales Location ................ 116 7.2 Drive-Time Analysis .......................................................... 119 7.3 Applying a Gravity Model to the Distance-Sales Analysis ........................................................................................................ 125 7.4 Monte Carlo Data on Retail Sales .................................... 129 7.5 Nonlinear Optimization and Gravity Model Estimation ........................................................................................................ 133 7.6 Gravity and Polynomial Models ....................................... 135 3 CHAPTER 8. Determining Sales and Market Areas ........................................................................................................ 140 8.1 Defining Market Areas ..................................................... 142 CHAPTER 9. Applications for Manufacturing ........ 148 CHAPTER 10. Fuzzy Logic Business Applications ... 155 CHAPTER 11. THE NATURE OF REGRESSION ANALYSIS 171 11.1 Historical origin of the term regression ......................... 171 11.2 Statistical versus deterministic relationships ................ 175 11.3 Types of Data ................................................................... 178 11.4 The Accuracy of Data ...................................................... 183 CHAPTER 12. TWO-VARIABLE REGRESSION ANALYSIS . 188 12.1 The sample regression function (SRF) .......................... 197 CHAPTER 13. Applications of Analytics in the Banking Sector ........................................................................ 203 13.1 Logistic Regression Equation ......................................... 209 13.2 Regression Model ............................................................ 214 13.2 Logistic Regression Model Using R .............................. 220 13.3 Model Building and Interpretation of Training and Testing Data .................................................................................. 226 CONCLUSIONS .......................................................................... 231 REFERENCES ........................................................................... 234 4 Introduction The primary objective of this book is to help economists, statisticians, developers, engineers, and data analysts who are well versed in writing codes; have a basic understanding of data and statistics; and are planning to transition to a data scientist profile. The most challenging part is practical and hands-on knowledge of building predictive models and machine learning algorithms and deploying them in industries to address industrial business problems. This book will benefit the reader in solving the business problems in various industrial domains by sharpening their analytical skills in getting practical exposure to various predictive model and machine learning algorithms This book focuses on industrial business problems and practical analytical approaches to solve those problems by implementing predictive models and machine learning techniques using MATLAB, and R analytical languages. Data analytics has become part and parcel of any business in today’s world. In fact, it has evolved into an industry in itself. Vast numbers of software platforms are available for data extraction, scrubbing, analysis, and visualization. Some of these platforms are specialized for carrying out one of the above-listed aspects of data analytics, while others offer a generalist tool to carry out almost all tasks ranging from data scrubbing to visualization. Who may read yhis book: 1. Economists, finance and valuation professionals, market researchers, public policy analysts, and other practitioners whose occupation demands the ability to model market behavior under a variety of real-world conditions 2. Teachers or students in graduate-level classes in applied economics, particularly those who cover topics such as fiscal and economic impact analysis, fuzzy logic applications, retail sales analysis, and the integration of geographic information systems, as this is one of the very few books to rigorously cover these subjects. Prerequisite Knowledge We presume the reader has the following knowledge: 1. A good grounding in the laws of microeconomics and a 5 familiarity with their application. Most practitioners and graduate students in economics, finance, public policy, and related disciplines should either be able to acquire this or should have already done so. Most undergraduates will need additional training. 2. A working understanding of mathematics, including (for some chapters) an understanding of the calculus used in comparative statistics. The completion of a graduate course in math for economists would be helpful, but is not mandatory. 3. For many of the chapters, a familiarity with (but not necessarily expertise in) the MATLAB software environment. Most of the applications presented here could be accomplished in other software environments, but we provide examples of applications in MATLAB. 4. For some chapters, an awareness (but not necessarily a working knowledge) of geographical information systems. What You Will Learn • Introduction to analytics and data understanding. • How to approach industrial business problems with an analytical approach. • Practical and hands-on knowledge in building predictive model and machine learning techniques. • Building the analytical strategies. Economic and fiscal impact models as well as tax policy and forecasting tax revenue are covered in Chapter 3, Chapter 4, and Chapter 5. These chapters provide practitioners in these fields with advanced tools that enable more accurate, reliable analysis. The first sections of the chapter on economic impact models identify the severe weaknesses in many common economic impact reports, adding in some examples of particularly absurd gross exaggeration in published reports. These sections close with a plea for ethics in that subfield. As much of our work has been in public policy, we use these tools on a regular basis and hope that the analytical tools will help improve the level of analysis and that our admonitions about ethics in using them will not fall on deaf ears. Chapter 6, Chapter 7, and Chapter 8 cover the economics of a firm. Chapter 9 describes modeling a firm, focusing on the crucial question