A number of clubs in professional sports leagues exhibit winning streaks over a number of consecutive seasons that do not conform to the standard economic model of a professional sports league developed by El Hodiri and Quirk (1994) and Fort and Quirk (1995). These clubs appear to display what we term "unsustainable runs", defined as a period of two to four seasons where the club acquires expensive talent and attempts to win a league championship despite not having the market size to sustain such a competitive position in the long run. The standard model predicts that clubs that locate in large economic markets will tend to acquire more talent, achieve more success on the field and at the box office than clubs that are located in small markets. This book builds a model that can allow unsustainable runs yet retains most of the features of the standard model. The model is then subjected to empirical verification. The new model we develop in the book has as its central feature the possibility of generating two equilibria for a club. In the empirical sections of the book, we use time-series analysis to attempt to test for the presence of unsustainable runs using historical data from National Football League (NFL), National Basketball Association (NBA), National Hockey League (NHL) and Major League Baseball (MLB). The multiple equilibria model retains all of the features of the standard model of a professional sports league that is accepted quite universally by economists, yet it offers a much richer approach by including an exploration of the effects of revenues that are earned at the league level (television, apparel, naming rights, etc.) that are then shared by all of the member clubs, making this book unique and of great interest to scholars in a variety of fields in economics.

​This publication provides insight into the agricultural sector. It illustrates new tendencies in agricultural economics and dynamics (interrelationship with other sectors in rural zones and multifunctionality) and the implications of the World Trade Organization negotiations in the international trade of agricultural products. Due to environmental problems, availability of budget, consumer preferences for food safety and pressure from the World Trade Organization, there are many changes in the agricultural sector. This book addresses those new developments and provides insights into possible future developments. The agricultural activity is an economic sector that is fundamental for a sustainable economic growth of every country. However, this sector has many particularities, namely those related with some structural problems (many farms with reduced dimension, sometimes lack of vocational training of the farmers, difficulties of put the farmers together in associations and cooperatives), variations of the productions and prices over the year and some environmental problems derived from the utilization of pesticides and fertilizers.

This edited book contains several state-of-the-art papers devoted to econometrics of risk. Some papers provide theoretical analysis of the corresponding mathematical, statistical, computational, and economical models. Other papers describe applications of the novel risk-related econometric techniques to real-life economic situations. The book presents new methods developed just recently, in particular, methods using non-Gaussian heavy-tailed distributions, methods using non-Gaussian copulas to properly take into account dependence between different quantities, methods taking into account imprecise ("fuzzy") expert knowledge, and many other innovative techniques. This versatile volume helps practitioners to learn how to apply new techniques of econometrics of risk, and researchers to further improve the existing models and to come up with new ideas on how to best take into account economic risks.

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.

This restructured, updated Third Edition provides a general overview of the econometrics of panel data, from both theoretical and applied viewpoints. Readers discover how econometric tools are used to study organizational and household behaviors as well as other macroeconomic phenomena such as economic growth. The book contains sixteen entirely new chapters; all other chapters have been revised to account for recent developments. With contributions from well known specialists in the field, this handbook is a standard reference for all those involved in the use of panel data in econometrics.

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 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.

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.

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.

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.

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.

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 Handbook provides an authoritative overview of current research in the field of cost-benefit analysis and is designed as a starting point for those interested in undertaking advanced research. The Handbook contains major contributions to the development of the field, focussing on standard microeconomic policy evaluations, the relatively neglected area of macroeconomic policy and its integration into a formal CBA framework, and dynamic considerations in CBA Presenting insights from many influential thinkers, and edited by a leading academic in the field, this comprehensive work will prove an invaluable reference tool for economists, researchers and scholars.

Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book s accompanying website.

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.

The Analytic Network Process (ANP), developed by Thomas Saaty in his work on multicriteria decision making, applies network structures with dependence and feedback to complex decision making. This new edition of Decision Making with the Analytic Network Process is a selection of the latest applications of ANP to economic, social and political decisions, and also to technological design. The ANP is a methodological tool that is helpful to organize knowledge and thinking, elicit judgments registered in both in memory and in feelings, quantify the judgments and derive priorities from them, and finally synthesize these diverse priorities into a single mathematically and logically justifiable overall outcome. In the process of deriving this outcome, the ANP also allows for the representation and synthesis of diverse opinions in the midst of discussion and debate. The book focuses on the application of the ANP in three different areas: economics, the social sciences and the linking of measurement with human values. Economists can use the ANP for an alternate approach for dealing with economic problems than the usual mathematical models on which economics bases its quantitative thinking. For psychologists, sociologists and political scientists, the ANP offers the methodology they have sought for some time to quantify and derive measurements for intangibles. Finally the book applies the ANP to provide people in the physical and engineering sciences with a quantitative method to link hard measurement to human values. In such a process, one is able to interpret the true meaning of measurements made on a uniform scale using a unit.

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.

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.

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.

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.

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.

The book's comprehensive coverage on the application of econometric methods to empirical analysis of economic issues is impressive. It uncovers the missing link between textbooks on economic theory and econometrics and highlights the powerful connection between economic theory and empirical analysis perfectly through examples on rigorous experimental design. The use of data sets for estimation derived with the Monte Carlo method helps facilitate the understanding of the role of hypothesis testing applied to economic models. Topics covered in the book are: consumer behavior, producer behavior, market equilibrium, macroeconomic models, qualitative-response models, panel data analysis and time-series analysis. Key econometric models are introduced, specified, estimated and evaluated. The treatment on methods of estimation in econometrics and the discipline of hypothesis testing makes it a must-have for graduate students of economics and econometrics and aids their understanding on how to estimate economic models and evaluate the results in terms of policy implications.

Many economic and social surveys are designed as panel studies, which provide important data for describing social changes and testing causal relations between social phenomena. This textbook shows how to manage, describe, and model these kinds of data. It presents models for continuous and categorical dependent variables, focusing either on the level of these variables at different points in time or on their change over time. It covers fixed and random effects models, models for change scores and event history models. All statistical methods are explained in an application-centered style using research examples from scholarly journals, which can be replicated by the reader through data provided on the accompanying website. As all models are compared to each other, it provides valuable assistance with choosing the right model in applied research. The textbook is directed at master and doctoral students as well as applied researchers in the social sciences, psychology, business administration and economics. Readers should be familiar with linear regression and have a good understanding of ordinary least squares estimation.

The Oxford Handbook of Computational Economics and Finance provides a survey of both the foundations of and recent advances in the frontiers of analysis and action. It is both historically and interdisciplinarily rich and also tightly connected to the rise of digital society. It begins with the conventional view of computational economics, including recent algorithmic development in computing rational expectations, volatility, and general equilibrium. It then moves from traditional computing in economics and finance to recent developments in natural computing, including applications of nature-inspired intelligence, genetic programming, swarm intelligence, and fuzzy logic. Also examined are recent developments of network and agent-based computing in economics. How these approaches are applied is examined in chapters on such subjects as trading robots and automated markets. The last part deals with the epistemology of simulation in its trinity form with the integration of simulation, computation, and dynamics. Distinctive is the focus on natural computationalism and the examination of the implications of intelligent machines for the future of computational economics and finance. Not merely individual robots, but whole integrated systems are extending their "immigration" to the world of Homo sapiens, or symbiogenesis.

Analyzing Event Statistics in Corporate Finance provides new alternative methodologies to increase accuracy when performing statistical tests for event studies within corporate finance. In contrast to conventional surveys or literature reviews, Jeng focuses on various methodological defects or deficiencies that lead to inaccurate empirical results, which ultimately produce bad corporate policies. This work discusses the issues of data collection and structure, the recursive smoothing for systematic components in excess returns, the choices of event windows, different time horizons for the events, and the consequences of applications of different methodologies. In providing improvement for event studies in corporate finance, and based on the fact that changes in parameters for financial time series are common knowledge, a new alternative methodology is developed to extend the conventional analysis to more robust arguments.