The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.

Spatial Econometrics provides a modern, powerful and flexible skillset to early career researchers interested in entering this rapidly expanding discipline. It articulates the principles and current practice of modern spatial econometrics and spatial statistics, combining rigorous depth of presentation with unusual depth of coverage. Introducing and formalizing the principles of, and 'need' for, models which define spatial interactions, the book provides a comprehensive framework for almost every major facet of modern science. Subjects covered at length include spatial regression models, weighting matrices, estimation procedures and the complications associated with their use. The work particularly focuses on models of uncertainty and estimation under various complications relating to model specifications, data problems, tests of hypotheses, along with systems and panel data extensions which are covered in exhaustive detail. Extensions discussing pre-test procedures and Bayesian methodologies are provided at length. Throughout, direct applications of spatial models are described in detail, with copious illustrative empirical examples demonstrating how readers might implement spatial analysis in research projects. Designed as a textbook and reference companion, every chapter concludes with a set of questions for formal or self--study. Finally, the book includes extensive supplementing information in a large sample theory in the R programming language that supports early career econometricians interested in the implementation of statistical procedures covered.

Technical Analysis of Stock Trends helps investors make smart, profitable trading decisions by providing proven long- and short-term stock trend analysis. It gets right to the heart of effective technical trading concepts, explaining technical theory such as The Dow Theory, reversal patterns, consolidation formations, trends and channels, technical analysis of commodity charts, and advances in investment technology. It also includes a comprehensive guide to trading tactics from long and short goals, stock selection, charting, low and high risk, trend recognition tools, balancing and diversifying the stock portfolio, application of capital, and risk management. This updated new edition includes patterns and modifiable charts that are tighter and more illustrative. Expanded material is also included on Pragmatic Portfolio Theory as a more elegant alternative to Modern Portfolio Theory; and a newer, simpler, and more powerful alternative to Dow Theory is presented. This book is the perfect introduction, giving you the knowledge and wisdom to craft long-term success.

The book proposes an overview of the research conducted to date in the field of wine economics. All of these contributions have in common the use of econometric techniques and mathematical formalization to describe the new challenges of this economic sector.

This book investigates the relationship between environmental degradation and income, focusing on carbon dioxide (CO2) emissions from around the world, to explore the possibility of sustainable development under global warming. Although many researchers have tackled this problem by estimating the Environmental Kuznets Curve (EKC), unlike the approach to sulfur dioxide emissions, there seems to be little consensus about whether EKC is formed with regard to CO2 emissions. Thus, EKC is one of the most controversial issues in the field of environmental economics. This book contributes three points with academic rigor. First, an unbalanced panel dataset containing over 150 countries with the latest CO2 emission data between 1960 and 2010 is constructed. Second, based on this dataset, the CO2 emission-income relationship is analyzed using strict econometric methods such as the dynamic panel model. Third, as it is often pointed out that some factors other than income affect CO2 emission, several variables were added to the estimation model to examine the effects of changes of industrial structure, energy composition, and overseas trade on CO2 emission.

The series is designed to bring together those mathematicians who are seriously interested in getting new challenging stimuli from economic theories with those economists who are seeking effective mathematical tools for their research. A lot of economic problems can be formulated as constrained optimizations and equilibration of their solutions. Various mathematical theories have been supplying economists with indispensable machineries for these problems arising in economic theory. Conversely, mathematicians have been stimulated by various mathematical difficulties raised by economic theories.

The research and its outcomes presented here focus on spatial sampling of agricultural resources. The authors introduce sampling designs and methods for producing accurate estimates of crop production for harvests across different regions and countries. With the help of real and simulated examples performed with the open-source software R, readers will learn about the different phases of spatial data collection. The agricultural data analyzed in this book help policymakers and market stakeholders to monitor the production of agricultural goods and its effects on environment and food safety.

This book is a comprehensive introduction of the reader into the simulation and modelling techniques and their application in the management of organisations. The book is rooted in the thorough understanding of systems theory applied to organisations and focuses on how this theory can apply to econometric models used in the management of organisations. The econometric models in this book employ linear and dynamic programming, graph theory, queuing theory, game theory, etc. and are presented and analysed in various fields of application, such as investment management, stock management, strategic decision making, management of production costs and the lifecycle costs of quality and non-quality products, production quality Management, etc.

The global demographic transition presents marked asymmetries as poor, emerging, and advanced countries are undergoing different stages of transition. Emerging countries are demographically younger than advanced economies. This youth is favorable to growth and generates a demographic dividend. However, the future of emerging economies will bring a decline in the working-age share and a rise in the older population, as is the case in today's developed world. Hence, developing countries must get rich before getting old, while advanced economies must try not to become poorer as they age. Asymmetric Demography and the Global Economy contributes to our understanding of why this demographic transition matters to the domestic macroeconomics and global capital movements affect the asset accumulation, growth potential, current account, and the economy's international investment position. This collaborative collection approaches these questions from the perspective of "systemically important" emerging countries i.e., members of the G20 but considers both the national and the global sides of the problem.

This is a book on deterministic and stochastic Growth Theory and the computational methods needed to produce numerical solutions. Exogenous and endogenous growth models are thoroughly reviewed. Special attention is paid to the use of these models for fiscal and monetary policy analysis. Modern Business Cycle Theory, the New Keynesian Macroeconomics, the class of Dynamic Stochastic General Equilibrium models, can be all considered as special cases of models of economic growth, and they can be analyzed by the theoretical and numerical procedures provided in the textbook. Analytical discussions are presented in full detail. The book is self contained and it is designed so that the student advances in the theoretical and the computational issues in parallel. EXCEL and Matlab files are provided on an accompanying website (see Preface to the Second Edition) to illustrate theoretical results as well as to simulate the effects of economic policy interventions. The structure of these program files is described in "Numerical exercise"-type of sections, where the output of these programs is also interpreted. The second edition corrects a few typographical errors and improves some notation.

Will history repeat itself, leaving Saudi Arabia to face another financial crisis due to drastic overspending and/or a dramatic drop in oil revenue? If the situation remains on its current trajectory, by 2030 government debt due to rising expenditures over revenues will be too overwhelming for the government to cope with.

Increasing concerns regarding the world's natural resources and sustainability continue to be a major issue for global development. As a result several political initiatives and strategies for green or resource-efficient growth both on national and international levels have been proposed. A core element of these initiatives is the promotion of an increase of resource or material productivity. This dissertation examines material productivity developments in the OECD and BRICS countries between 1980 and 2008. By applying the concept of convergence stemming from economic growth theory to material productivity the analysis provides insights into both aspects: material productivity developments in general as well potentials for accelerated improvements in material productivity which consequently may allow a reduction of material use globally. The results of the convergence analysis underline the importance of policy-making with regard to technology and innovation policy enabling the production of resource-efficient products and services as well as technology transfer and diffusion.

The purpose of this book is to establish a connection between the traditional field of empirical economic research and the emerging area of empirical financial research and to build a bridge between theoretical developments in these areas and their application in practice. Accordingly, it covers broad topics in the theory and application of both empirical economic and financial research, including analysis of time series and the business cycle; different forecasting methods; new models for volatility, correlation and of high-frequency financial data and new approaches to panel regression, as well as a number of case studies. Most of the contributions reflect the state-of-art on the respective subject. The book offers a valuable reference work for researchers, university instructors, practitioners, government officials and graduate and post-graduate students, as well as an important resource for advanced seminars in empirical economic and financial research.

This peer reviewed volume is part of an annual series, dedicated to the presentation and discussion of state of the art studies in the application of management science to the solution of significant managerial decision making problems. It is hoped that this research annual will significantly aid in the dissemination of actual applications of management science in both the public and private sectors. Volume 11 is directed toward the applications of mathematical programming to (1) Multi-criteria decision making, (2) Supply chain management, (3) Performance management, and (4) Risk analysis. Its use can be found both in university classes in management science and operations research, (management and engineering schools), as well as to both the researcher and practitioner of management science and operations research.

The objective of this book is the discussion and the practical illustration of techniques used in applied macroeconometrics. There are currently three competing approaches: the LSE (London School of Economics) approach, the VAR approach, and the intertemporal optimization/Real Business Cycle approach. This book discusses and illustrates the empirical research strategy of these three alternative approaches, pairing them with extensive discussions and replications of the relevant empirical work. Common benchmarks are used to evaluate the alternative approaches.

This textbook provides a coherent introduction to the main concepts and methods of one-parameter statistical inference. Intended for students of Mathematics taking their first course in Statistics, the focus is on Statistics for Mathematicians rather than on Mathematical Statistics. The goal is not to focus on the mathematical/theoretical aspects of the subject, but rather to provide an introduction to the subject tailored to the mindset and tastes of Mathematics students, who are sometimes turned off by the informal nature of Statistics courses. This book can be used as the basis for an elementary semester-long first course on Statistics with a firm sense of direction that does not sacrifice rigor. The deeper goal of the text is to attract the attention of promising Mathematics students.

A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance. Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).

Drawing on a lifetime of distinguished work in economic research and policy-making, Andrew Kamarck details how his profession can more usefully analyze and solve economic problems by changing its basic approach to research.Kamarck contends that most economists today strive for a mathematical precision in their work that neither stems from nor leads to an accurate view of economic reality. He develops elegant critiques of key areas of economic analysis based on appreciation of scientific method and knowledge of the limitations of economic data. Concepts such as employment, market, and money supply must be seen as loose, not exact. Measurement of national income becomes highly problematic when raking into account such factors as the "underground economy" and currency differences. World trade analysis is based on inconsistent and often inaccurate measurements. Subtle realities of the individual, social, and political worlds render largely ineffective both large-scale macroeconomics models and micro models of the consumer and the firm. Fashionable cost-benefit analysis must be recognized as inherently imprecise. Capital and investment in developing countries tend to be measured in easy but irrelevant ways.Kamarck concludes with a call for economists to involve themselves in data collection, to insist on more accurate and reliable data sources, to do analysis within the context of experience, and to take a realistic, incremental approach to policy-making. Kamarck's concerns are shared by many economists, and his eloquent presentation will be essential reading for his colleagues and for those who make use of economic research.

Upon the backdrop of impressive progress made by the Indian economy during the last two decades after the large-scale economic reforms in the early 1990s, this book evaluates the performance of the economy on some income and non-income dimensions of development at the national, state and sectoral levels. It examines regional economic growth and inequality in income originating from agriculture, industry and services. In view of the importance of the agricultural sector, despite its declining share in gross domestic product, it evaluates the performance of agricultural production and the impact of agricultural reforms on spatial integration of food grain markets. It studies rural poverty, analyzing the trend in employment, the trickle-down process and the inclusiveness of growth in rural India. It also evaluates the impact of microfinance, as an instrument of financial inclusion, on the socio-economic conditions of rural households. Lastly, it examines the relative performance of fifteen major states of India in terms of education, health and human development. An important feature of the book is that it approaches these issues, applying rigorously advanced econometric methods, and focusing primarily on their regional disparities during the post-reform period vis-a-vis the pre-reform period. It offers important results to guide policies for future development.

This book focuses on general frameworks for modeling heavy-tailed distributions in economics, finance, econometrics, statistics, risk management and insurance. A central theme is that of (non-)robustness, i.e., the fact that the presence of heavy tails can either reinforce or reverse the implications of a number of models in these fields, depending on the degree of heavy-tailed ness. These results motivate the development and applications of robust inference approaches under heavy tails, heterogeneity and dependence in observations. Several recently developed robust inference approaches are discussed and illustrated, together with applications.

Originally published in 1939, this book forms the second part of a two-volume series on the mathematics required for the examinations of the Institute of Actuaries, focusing on finite differences, probability and elementary statistics. Miscellaneous examples are included at the end of the text. This book will be of value to anyone with an interest in actuarial science and mathematics.

The book is a collection of essays in honour of Clive Granger. The chapters are by some of the world's leading econometricians, all of whom have collaborated with or studied with (or both) Clive Granger. Central themes of Granger's work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.

Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena. The artificial intelligence techniques used to model economic data include: multi-layer perceptron neural networks radial basis functions support vector machines rough sets genetic algorithm particle swarm optimization simulated annealing multi-agent system incremental learning fuzzy networks Signal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace - and vice versa - is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation. Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.

This book gives an introduction to R to build up graphing, simulating and computing skills to enable one to see theoretical and statistical models in economics in a unified way. The great advantage of R is that it is free, extremely flexible and extensible. The book addresses the specific needs of economists, and helps them move up the R learning curve. It covers some mathematical topics such as, graphing the Cobb-Douglas function, using R to study the Solow growth model, in addition to statistical topics, from drawing statistical graphs to doing linear and logistic regression. It uses data that can be downloaded from the internet, and which is also available in different R packages. With some treatment of basic econometrics, the book discusses quantitative economics broadly and simply, looking at models in the light of data. Students of economics or economists keen to learn how to use R would find this book very useful.

Mathematical Statistics for Economics and Business, Second Edition, provides a comprehensive introduction to the principles of mathematical statistics which underpin statistical analyses in the fields of economics, business, and econometrics. The selection of topics in this textbook is designed to provide students with a conceptual foundation that will facilitate a substantial understanding of statistical applications in these subjects. This new edition has been updated throughout and now also includes a downloadable Student Answer Manual containing detailed solutions to half of the over 300 end-of-chapter problems. After introducing the concepts of probability, random variables, and probability density functions, the author develops the key concepts of mathematical statistics, most notably: expectation, sampling, asymptotics, and the main families of distributions. The latter half of the book is then devoted to the theories of estimation and hypothesis testing with associated examples and problems that indicate their wide applicability in economics and business. Features of the new edition include: a reorganization of topic flow and presentation to facilitate reading and understanding; inclusion of additional topics of relevance to statistics and econometric applications; a more streamlined and simple-to-understand notation for multiple integration and multiple summation over general sets or vector arguments; updated examples; new end-of-chapter problems; a solution manual for students; a comprehensive answer manual for instructors; and a theorem and definition map. This book has evolved from numerous graduate courses in mathematical statistics and econometrics taught by the author, and will be ideal for students beginning graduate study as well as for advanced undergraduates.