About the Book: The revised second edition thoroughly explains the basic methods and techniques involved in mathematical analysis of economic phenomena. Statistical methods have been emphasized. Numerous new concepts, solved examples and illustrative practice problems have been included throughout the book. In addition, few new chapters have been added to enrich the quality of text as well. About the Author: Dr. R. Veerachamy was formerly Professor and Chairman in the Department of Economics, Bangalore University, Bangalore. He has put in 37 years of teaching on Quantitative Techniques for both Economics and Management students. His book "Quantitative Methods for Economists" is a very popular text among student community all over the country. Since 1973 he is handling the paper "Quantitative Methods for Economists" for the postgraduate students in Bangalore University. He has obtained his MSc. degree in Mathematical Economics from Madurai Kamaraj University and also has MA Economics degree as well from the same University to his credit. He is a First Rank Gold Medalist in Econometrics. He received his Ph.D. in the area of International Economics from Bangalore University. He has contributed research papers/articles in several journals of repute. He has vast experience in curriculum development for both MA and MBA courses. Currently he is working as a Professor, Department of Management Studies, East Point College of Higher Education, Bangalore.

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice.

An integrated approach to the empirical application of dynamic optimization programming models, for students and researchers. This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics. In part I the authors first review the formal theory of dynamic optimization; they then present the numerical tools and econometric techniques necessary to evaluate the theoretical models. In language accessible to a reader with a limited background in econometrics, they explain most of the methods used in applied dynamic research today, from the estimation of probability in a coin flip to a complicated nonlinear stochastic structural model. These econometric techniques provide the final link between the dynamic programming problem and data. Part II is devoted to the application of dynamic programming to specific areas of applied economics, including the study of business cycles, consumption, and investment behavior. In each instance the authors present the specific optimization problem as a dynamic programming problem, characterize the optimal policy functions, estimate the parameters, and use models for policy evaluation. The original contribution of Dynamic Economics: Quantitative Methods and Applications lies in the integrated approach to the empirical application of dynamic optimization programming models. This integration shows that empirical applications actually complement the underlying theory of optimization, while dynamic programming problems provide needed structure for estimation and policy evaluation.

The purpose of the Special Issue “Quantitative Methods in Economics and Finance” of the journal Risks was to provide a collection of papers that reflect the latest research and problems of pricing complex derivates, simulation pricing, analysis of financial markets, and volatility of exchange rates in the international context. This book can be used as a reference for academicians and researchers who would like to discuss and introduce new developments in the field of quantitative methods in economics and finance and explore applications of quantitative methods in other business areas.

This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.

Contemporary economists, when analyzing economic behavior of people, need to use the diversity of research methods and modern ways of discovering knowledge. The increasing popularity of using economic experiments requires the use of IT tools and quantitative methods that facilitate the analysis of the research material obtained as a result of the experiments and the formulation of correct conclusions. This proceedings volume presents problems in contemporary economics and provides innovative solutions using a range of quantitative and experimental tools. Featuring selected contributions presented at the 2018 Computational Methods in Experimental Economics Conference (CMEE 2018), this book provides a modern economic perspective on such important issues as: sustainable development, consumption, production, national wealth, the silver economy, behavioral finance, economic and non-economic factors determining the behavior of household members, consumer preferences, social campaigns, and neuromarketing. International case studies are also offered.

Economic Theory, Econometrics, and Mathematical Economics: New Quantitative Techniques for Economic Analysis provides a critical appraisal of the results, the limits, and the developments of well-established quantitative techniques. This book presents a detailed analysis of the quantitative techniques for economic analysis. Organized into four parts encompassing 16 chapters, this book begins with an overview of the general questions concerning models and model making. This text then provides the main results and various interesting economic applications of some quantitative techniques that have not been widely used in the economic field. Other chapters consider the principle of optimality in dynamic programing wherein the infinite sequence of consumption-saving decisions can be reduced to one decision. This book discusses as well the methods for online control and management of large-scale systems. The final chapter deals with special problems. This book is a valuable resource for economists, social scientists, epistemologists, economic historians, and research workers.