This book is a collection of selected papers presented at the Annual Meeting of the European Academy of Management and Business Economics (AEDEM), held at the Faculty of Economics and Business of the University of Barcelona, 05 07 June, 2012. This edition of the conference has been presented with the slogan Creating new opportunities in an uncertain environment . There are different ways for assessing uncertainty in management but this book mainly focused on soft computing theories and their role in assessing uncertainty in a complex world.

The present book gives a comprehensive overview of general management topics and discusses some of the most recent developments in all the areas of business and management including management, marketing, business statistics, innovation and technology, finance, sports and tourism. This book might be of great interest for anyone working in the area of management and business economics and might be especially useful for scientists and graduate students doing research in these fields."

This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences.

This book brings together the latest research in the areas of market microstructure and high-frequency finance along with new econometric methods to address critical practical issues in these areas of research. Thirteen chapters, each of which makes a valuable and significant contribution to the existing literature have been brought together, spanning a wide range of topics including information asymmetry and the information content in limit order books, high-frequency return distribution models, multivariate volatility forecasting, analysis of individual trading behaviour, the analysis of liquidity, price discovery across markets, market microstructure models and the information content of order flow. These issues are central both to the rapidly expanding practice of high frequency trading in financial markets and to the further development of the academic literature in this area. The volume will therefore be of immediate interest to practitioners and academics. This book was originally published as a special issue of European Journal of Finance.

This is the seventh book in a series of discussions about the great minds in the history and theory of finance. While the series addresses the contributions of scholars in our understanding of financial decisions and markets, this seventh book describes how econometrics developed and how its underlying assumptions created the underpinning of much of modern financial theory. The author shows that the theorists of econometrics were a mix of mathematicians and cosmologists, entrepreneurs, economists and financial scholars. The author demonstrates that by laying down the foundation of empirical analysis, they also forever determined the way in which we think about financial returns and the vocabulary we employ to describe them. Through this volume, the reader can discover the life stories, inspirations, and theories of Carl Friedrich Gauss, Francis Galton, Karl Pearson, Ronald Aylmer Fisher, Harold Hotelling, Alfred Cowles III, Ragnar Frisch, and Trygve Haavelmo, specifically. We learn how each theorist made an intellectual leap simply by thinking about a conventional problem in an unconventional way.

This book studies the information spillover among financial markets and explores the intraday effect and ACD models with high frequency data. This book also contributes theoretically by providing a new statistical methodology with comparative advantages for analyzing co-movements between two time series. It explores this new method by testing the information spillover between the Chinese stock market and the international market, futures market and spot market. Using the high frequency data, this book investigates the intraday effect and examines which type of ACD model is particularly suited in capturing financial duration dynamics.

The book will be of invaluable use to scholars and graduate students interested in co-movements among different financial markets and financial market microstructure and to investors and regulation departments looking to improve their risk management.

First published in 1994. Concepts of probability are an integral component of economic theory. However there are a wide range of theories of probability and these are manifested in different approaches to economic theory itself. In this book Charles McCann, Jr provides a clear and informative survey of the area which serves to standardize terminology and so integrate probability into a discussion of the foundations of economic theory. This is illustrated by examples from Austrian, Keynesian and New Classical Economics.

First Published in 1970. Econometric model-building, on the other hand, has been largely confined to the advanced industrialised countries. In the few cases where macro-models have been built for underdeveloped countries (e.g. the Narasimham model (112) for India) the underlying assumptions have been largely of the Keynesian type, and thus in the authors opinion unconnected with the theory of economic development. This study is a modest attempt at econometric model-building on the basis of a model of development of an underdeveloped country.

'Big data' is now readily available to economic historians, thanks to the digitisation of primary sources, collaborative research linking different data sets, and the publication of databases on the internet. Key economic indicators, such as the consumer price index, can be tracked over long periods, and qualitative information, such as land use, can be converted to a quantitative form. In order to fully exploit these innovations it is necessary to use sophisticated statistical techniques to reveal the patterns hidden in datasets, and this book shows how this can be done. A distinguished group of economic historians have teamed up with younger researchers to pilot the application of new techniques to 'big data'. Topics addressed in this volume include prices and the standard of living, money supply, credit markets, land values and land use, transport, technological innovation, and business networks. The research spans the medieval, early modern and modern periods. Research methods include simultaneous equation systems, stochastic trends and discrete choice modelling. This book is essential reading for doctoral and post-doctoral researchers in business, economic and social history. The case studies will also appeal to historical geographers and applied econometricians.

Portfolio theory and much of asset pricing, as well as many empirical applications, depend on the use of multivariate probability distributions to describe asset returns. Traditionally, this has meant the multivariate normal (or Gaussian) distribution. More recently, theoretical and empirical work in financial economics has employed the multivariate Student (and other) distributions which are members of the elliptically symmetric class. There is also a growing body of work which is based on skew-elliptical distributions. These probability models all exhibit the property that the marginal distributions differ only by location and scale parameters or are restrictive in other respects. Very often, such models are not supported by the empirical evidence that the marginal distributions of asset returns can differ markedly. Copula theory is a branch of statistics which provides powerful methods to overcome these shortcomings. This book provides a synthesis of the latest research in the area of copulae as applied to finance and related subjects such as insurance. Multivariate non-Gaussian dependence is a fact of life for many problems in financial econometrics. This book describes the state of the art in tools required to deal with these observed features of financial data. This book was originally published as a special issue of the European Journal of Finance.

In the twentieth century, Americans thought of the United States as a land of opportunity and equality. To what extent and for whom this was true was, of course, a matter of debate, however especially during the Cold War, many Americans clung to the patriotic conviction that America was the land of the free. At the same time, another national ideal emerged that was far less contentious, that arguably came to subsume the ideals of freedom, opportunity, and equality, and that eventually embodied an unspoken consensus about what constitutes the good society in a postmodern setting. This was the ideal of choice, broadly understood as the proposition that the good society provides individuals with the power to shape the contours of their lives in ways that suit their personal interests, idiosyncrasies, and tastes. By the closing decades of the century, Americans were widely agreed that theirs was-or at least should be-the land of choice. In A Destiny of Choice?, David Blanke and David Steigerwald bring together important scholarship on the tension between two leading interpretations of modern American consumer culture. That modern consumerism reflects the social, cultural, economic, and political changes that accompanied the country's transition from a local, producer economy dominated by limited choices and restricted credit to a national consumer marketplace based on the individual selection of mass-produced, mass-advertised, and mass-distributed goods. This debate is central to the economic difficulties seen in the United States today.

This book presents models and statistical methods for the analysis of recurrent event data. The authors provide broad, detailed coverage of the major approaches to analysis, while emphasizing the modeling assumptions that they are based on. More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions. Parametric, nonparametric and semiparametric methodologies are all covered, with procedures for estimation, testing and model checking.

This book constitutes the first serious attempt to explain the basics of econometrics and its applications in the clearest and simplest manner possible. Recognising the fact that a good level of mathematics is no longer a necessary prerequisite for economics/financial economics undergraduate and postgraduate programmes, it introduces this key subdivision of economics to an audience who might otherwise have been deterred by its complex nature.

With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. A companion website with downloadable datasets, exercises and lecture slides rounds out the full learning package.

This book covers the basics of processing and spectral analysis of monovariate discrete-time signals. The approach is practical, the aim being to acquaint the reader with the indications for and drawbacks of the various methods and to highlight possible misuses. The book is rich in original ideas, visualized in new and illuminating ways, and is structured so that parts can be skipped without loss of continuity. Many examples are included, based on synthetic data and real measurements from the fields of physics, biology, medicine, macroeconomics etc., and a complete set of MATLAB exercises requiring no previous experience of programming is provided. Prior advanced mathematical skills are not needed in order to understand the contents: a good command of basic mathematical analysis is sufficient. Where more advanced mathematical tools are necessary, they are included in an Appendix and presented in an easy-to-follow way. With this book, digital signal processing leaves the domain of engineering to address the needs of scientists and scholars in traditionally less quantitative disciplines, now facing increasing amounts of data.

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.

Essentials of Applied Econometrics prepares students for a world in which more data surround us every day and in which econometric tools are put to diverse uses. Written for students in economics and for professionals interested in continuing an education in econometrics, this succinct text not only teaches best practices and state-of-the-art techniques, but uses vivid examples and data obtained from a variety of real world sources. The book's emphasis on application uniquely prepares the reader for today's econometric work, which can include analyzing causal relationships or correlations in big data to obtain useful insights.

Building on the strength of the first edition, Quantitative Methods for Business and Economics provides a simple introduction to the mathematical and statistical techniques needed in business. This book is accessible and easy to use, with the emphasis clearly on how to apply quantitative techniques to business situations. It includes numerous real world applications and many opportunities for student interaction. It is clearly focused on business, management and economics students taking a single module in Quantitative Methods.

"Econometric Theory" presents a modern approach to the theory of econometric estimation and inference, with particular applications to time series. An ideal reference for practitioners and researchers, the book is also suited for advanced two-semester econometrics courses and one-semester regression courses.

Based on lectures originally given to graduates at the London School of Economics, the book applies recent developments in asymptotic theory to derive the properties of estimators when the model is only partially specified. Topics covered in depth include the linear regression model, dynamic modeling, simultaneous equations, optimization estimators, hypothesis testing, and the theory of nonstationary time series and cointegration.

Heavy tails -extreme events or values more common than expected -emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks the natural emergence of heavy-tailed distributions from a wide variety of general processes, building intuition. And it reveals the controversy surrounding heavy tails to be the result of flawed statistics, then equips readers to identify and estimate with confidence. Over 100 exercises complete this engaging package.

Sociological theories of crime include: theories of strain blame crime on personal stressors; theories of social learning blame crime on its social rewards, and see crime more as an institution in conflict with other institutions rather than as in- vidual deviance; and theories of control look at crime as natural and rewarding, and explore the formation of institutions that control crime. Theorists of corruption generally agree that corruption is an expression of the Patron-Client relationship in which a person with access to resources trades resources with kin and members of the community in exchange for loyalty. Some approaches to modeling crime and corruption do not involve an explicit simulation: rule based systems; Bayesian networks; game theoretic approaches, often based on rational choice theory; and Neoclassical Econometrics, a rational choice-based approach. Simulation-based approaches take into account greater complexities of interacting parts of social phenomena. These include fuzzy cognitive maps and fuzzy rule sets that may incorporate feedback; and agent-based simulation, which can go a step farther by computing new social structures not previously identified in theory. The latter include cognitive agent models, in which agents learn how to perceive their en- ronment and act upon the perceptions of their individual experiences; and reactive agent simulation, which, while less capable than cognitive-agent simulation, is adequate for testing a policy's effects with existing societal structures. For example, NNL is a cognitive agent model based on the REPAST Simphony toolkit.

Across the social sciences there has been increasing focus on reproducibility, i.e., the ability to examine a study's data and methods to ensure accuracy by reproducing the study. Reproducible Econometrics Using R combines an overview of key issues and methods with an introduction to how to use them using open source software (R) and recently developed tools (R Markdown and bookdown) that allow the reader to engage in reproducible econometric research. Jeffrey S. Racine provides a step-by-step approach, and covers five sets of topics, i) linear time series models, ii) robust inference, iii) robust estimation, iv) model uncertainty, and v) advanced topics. The time series material highlights the difference between time-series analysis, which focuses on forecasting, versus cross-sectional analysis, where the focus is typically on model parameters that have economic interpretations. For the time series material, the reader begins with a discussion of random walks, white noise, and non-stationarity. The reader is next exposed to the pitfalls of using standard inferential procedures that are popular in cross sectional settings when modelling time series data, and is introduced to alternative procedures that form the basis for linear time series analysis. For the robust inference material, the reader is introduced to the potential advantages of bootstrapping and the Jackknifing versus the use of asymptotic theory, and a range of numerical approaches are presented. For the robust estimation material, the reader is presented with a discussion of issues surrounding outliers in data and methods for addressing their presence. Finally, the model uncertainly material outlines two dominant approaches for dealing with model uncertainty, namely model selection and model averaging. Throughout the book there is an emphasis on the benefits of using R and other open source tools for ensuring reproducibility. The advanced material covers machine learning methods (support vector machines that are useful for classification) and nonparametric kernel regression which provides the reader with more advanced methods for confronting model uncertainty. The book is well suited for advanced undergraduate and graduate students alike. Assignments, exams, slides, and a solution manual are available for instructors.

This two volume set is a collection of 30 classic papers presenting ideas which have now become standard in the field of Bayesian inference. Topics covered include the central field of statistical inference as well as applications to areas of probability theory, information theory, utility theory and computational theory. It is organized into seven sections: foundations, information theory and prior distributions; robustness and outliers; hierarchical, multivariate and non-parametric models; asymptotics; computations and Monte Carlo methods; and Bayesian econometrics.

In recent years econometricians have examined the problems of diagnostic testing, specification testing, semiparametric estimation and model selection. In addition researchers have considered whether to use model testing and model selection procedures to decide the models that best fit a particular dataset. This book explores both issues with application to various regression models, including the arbitrage pricing theory models. It is ideal as a reference for statistical sciences postgraduate students, academic researchers and policy makers in understanding the current status of model building and testing techniques.

How do groups form, how do institutions come into being, and when do moral norms and practices emerge? This volume explores how game-theoretic approaches can be extended to consider broader questions that cross scales of organization, from individuals to cooperatives to societies. Game theory' strategic formulation of central problems in the analysis of social interactions is used to develop multi-level theories that examine the interplay between individuals and the collectives they form. The concept of cooperation is examined at a higher level than that usually addressed by game theory, especially focusing on the formation of groups and the role of social norms in maintaining their integrity, with positive and negative implications. The authors suggest that conventional analyses need to be broadened to explain how heuristics, like concepts of fairness, arise and become formalized into the ethical principles embraced by a society.

Economists are regularly confronted with results of quantitative economics research. Econometrics: Theory and Applications with EViews provides a broad introduction to quantitative economic methods, for example how models arise, their underlying assumptions and how estimates of parameters or other economic quantities are computed. The author combines econometric theory with practice by demonstrating its use with the software package EViews through extensive use of screen shots. The emphasis is on understanding how to select the right method of analysis for a given situation, and how to actually apply the theoretical methodology correctly. The EViews software package is available from 'Quantitive Micro Software'. Written for any undergraduate or postgraduate course in Econometrics.