This book presents a systematic overview of cutting-edge research in the field of parametric modeling of personal income and wealth distribution, which allows one to represent how income/wealth is distributed within a given population. The estimated parameters may be used to gain insights into the causes of the evolution of income/wealth distribution over time, or to interpret the differences between distributions across countries. Moreover, once a given parametric model has been fitted to a data set, one can straightforwardly compute inequality and poverty measures. Finally, estimated parameters may be used in empirical modeling of the impact of macroeconomic conditions on the evolution of personal income/wealth distribution. In reviewing the state of the art in the field, the authors provide a thorough discussion of parametric models belonging to the " -generalized" family, a new and fruitful set of statistical models for the size distribution of income and wealth that they have developed over several years of collaborative and multidisciplinary research. This book will be of interest to all who share the belief that problems of income and wealth distribution merit detailed conceptual and methodological attention.

Institutions are the formal or informal 'rules of the game' that facilitate economic, social, and political interactions. These include such things as legal rules, property rights, constitutions, political structures, and norms and customs. The main theoretical insights from Austrian economics regarding private property rights and prices, entrepreneurship, and spontaneous order mechanisms play a key role in advancing institutional economics. The Austrian economics framework provides an understanding for which institutions matter for growth, how they matter, and how they emerge and can change over time. Specifically, Austrians have contributed significantly to the areas of institutional stickiness and informal institutions, self-governance and self-enforcing contracts, institutional entrepreneurship, and the political infrastructure for development.

An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. Nonparametric Finance provides graduate students and finance professionals with a foundation in nonparametric function estimation and the underlying mathematics. Combining practical applications, mathematically rigorous presentation, and statistical data analysis into a single volume, this book presents detailed instruction in discrete chapters that allow readers to dip in as needed without reading from beginning to end. Coverage includes statistical finance, risk management, portfolio management, and securities pricing to provide a practical knowledge base, and the introductory chapter introduces basic finance concepts for readers with a strictly mathematical background. Economic significance is emphasized over statistical significance throughout, and R code is provided to help readers reproduce the research, computations, and figures being discussed. Strong graphical content clarifies the methods and demonstrates essential visualization techniques, while deep mathematical and statistical insight backs up practical applications. Written for the leading edge of finance, Nonparametric Finance: - Introduces basic statistical finance concepts, including univariate and multivariate data analysis, time series analysis, and prediction - Provides risk management guidance through volatility prediction, quantiles, and value-at-risk - Examines portfolio theory, performance measurement, Markowitz portfolios, dynamic portfolio selection, and more - Discusses fundamental theorems of asset pricing, Black-Scholes pricing and hedging, quadratic pricing and hedging, option portfolios, interest rate derivatives, and other asset pricing principles - Provides supplementary R code and numerous graphics to reinforce complex content Nonparametric function estimation has received little attention in the context of risk management and option pricing, despite its useful applications and benefits. This book provides the essential background and practical knowledge needed to take full advantage of these little-used methods, and turn them into real-world advantage. Jussi Klemela, PhD, is Adjunct Professor at the University of Oulu. His research interests include nonparametric function estimation, density estimation, and data visualization. He is the author of Smoothing of Multivariate Data: Density Estimation and Visualization and Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance.

Are you innately curious about dynamically inter-operating financial markets? Since the crisis of 2008, there is a need for professionals with more understanding about statistics and data analysis, who can discuss the various risk metrics, particularly those involving extreme events. By providing a resource for training students and professionals in basic and sophisticated analytics, this book meets that need. It offers both the intuition and basic vocabulary as a step towards the financial, statistical, and algorithmic knowledge required to resolve the industry problems, and it depicts a systematic way of developing analytical programs for finance in the statistical language R. Build a hands-on laboratory and run many simulations. Explore the analytical fringes of investments and risk management. Bennett and Hugen help profit-seeking investors and data science students sharpen their skills in many areas, including time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.

Offers a practical introduction to regression modeling with spatial and spatial-temporal data relevant to research and teaching in the social and economic sciences Focuses on a few key datasets and data analysis using the open source software WinBUGS, R, and GeoDa Provides data and programming codes to allow users to undertake their own analyses Ends each chapter with a set of short exercises and questions for further study

One of the major problems of macroeconomic theory is the way in which the people exchange goods in decentralized market economies. There are major disagreements among macroeconomists regarding tools to influence required outcomes. Since the mainstream efficient market theory fails to provide an internal coherent framework, there is a need for an alternative theory. The book provides an innovative approach for the analysis of agent based models, populated by the heterogeneous and interacting agents in the field of financial fragility. The text is divided in two parts; the first presents analytical developments of stochastic aggregation and macro-dynamics inference methods. The second part introduces macroeconomic models of financial fragility for complex systems populated by heterogeneous and interacting agents. The concepts of financial fragility and macroeconomic dynamics are explained in detail in separate chapters. The statistical physics approach is applied to explain theories of macroeconomic modelling and inference.

Originally published in 1954, on behalf of the National Institute of Economic and Social Research, this book presents a general review of British economic statistics in relation to the uses made of them for policy purposes. The text begins with an examination, in general terms, of the ways in which statistics can help in guiding or assessing policy, covering housing, coal, the development areas, agricultural price-fixing, the balance of external payments and the balance of the economy. The problems of statistical application are then separately discussed under the headings of quality, presentation and availability, and organization. A full bibliography and reference table of principal British economic statistics are also included. This book will be of value to anyone with an interest in British economic history and statistics.

This book discusses the integrated concepts of statistical quality engineering and management tools. It will help readers to understand and apply the concepts of quality through project management and technical analysis, using statistical methods. Prepared in a ready-to-use form, the text will equip practitioners to implement the Six Sigma principles in projects. The concepts discussed are all critically assessed and explained, allowing them to be practically applied in managerial decision-making, and in each chapter, the objectives and connections to the rest of the work are clearly illustrated. To aid in understanding, the book includes a wealth of tables, graphs, descriptions and checklists, as well as charts and plots, worked-out examples and exercises. Perhaps the most unique feature of the book is its approach, using statistical tools, to explain the science behind Six Sigma project management and integrated in engineering concepts. The material on quality engineering and statistical management tools offers valuable support for undergraduate, postgraduate and research students. The book can also serve as a concise guide for Six Sigma professionals, Green Belt, Black Belt and Master Black Belt trainers.

This book reviews the three most popular methods (and their extensions) in applied economics and other social sciences: matching, regression discontinuity, and difference in differences. The book introduces the underlying econometric/statistical ideas, shows what is identified and how the identified parameters are estimated, and then illustrates how they are applied with real empirical examples. The book emphasizes how to implement the three methods with data: many data and programs are provided in the online appendix. All readers--theoretical econometricians/statisticians, applied economists/social-scientists and researchers/students--will find something useful in the book from different perspectives.

Originally published in 1932, as part of the Institute of Actuaries Students' Society's Consolidation of Reading Series, this book was written to provide actuarial students with a guide 'to bridging the gap between the strict mathematics of life contingencies and the severely practical problems of Life Office Valuations'. This book will be of value to anyone with an interest in the actuarial profession and the history of finance.

Originally published in 1931, this book was written to provide actuarial students with a guide to mathematics, with information on elementary trigonometry, finite differences, summation, differential and integral calculus, and probability. Examples are included throughout. This book will be of value to anyone with an interest in actuarial practice and its relationship with aspects of mathematics.

Originally published in 1939, this book forms the first part of a two-volume series on the mathematics required for the examinations of the Institute of Actuaries, focusing on elementary differential and integral calculus. 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.

Originally published in 1930, this book was formed from the content of three lectures delivered at London University during March of that year. The text provides a concise discussion of the relationship between theoretical statistics and actuarial science. This book will be of value to anyone with an interest in the actuarial profession, statistics and the history of finance.

Denmark is set to achieve 100 per cent renewable energy by 2030. Iceland has topped the gender equality rankings for a decade and counting. South Korea’s average life expectancy will soon reach ninety. How have these places achieved such remarkable outcomes? And how can we apply those lessons to our own communities? The future we want is already here - it's just not evenly distributed. By bringing together for the first time tried and tested solutions to society's most pressing problems, from violence to inequality, Andrew Wear shows that the world we want to live in is already within reach. Solved is a much-needed dose of optimism in an atmosphere of doom and gloom. Informative, accessible and revelatory, it is a celebration of the power of human ingenuity to make the future brighter for everyone.

Jeder Kredit birgt fur den Kreditgeber ein Risiko, da unsicher ist, ob der Kreditnehmer seinen Zahlungsverpflichtungen nachkommen wird. Gemessen wird dieses Kreditrisiko mit Hilfe statistischer Methoden. Vor dem Hintergrund Basel II hat die Kreditrisikomessung an Bedeutung gewonnen. Dieses Buch schliesst die Lucke zwischen statistischer Grundlagenliteratur und mathematisch anspruchsvollen Werken. Es bietet einen Einstieg in die Kreditrisikomessung und die dafur notwendige Statistik. Ausgehend von den wichtigsten Begriffen zum Kreditrisiko werden deren statistische Analoga beschrieben. Enthalten sind relevante statistische Verteilungen und eine Einfuhrung in stochastische Prozesse, Portfoliomodelle und Score- bzw. Ratingmodelle. Zahlreiche praxisnahe Beispiele ermoeglichen den idealen Einstieg fur Praktiker und Quereinsteiger.

This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Among the topics treated are current status and interval censoring models, competing risk models, and deconvolution. Methods of order restricted inference are used in computing maximum likelihood estimators and developing distribution theory for inverse problems of this type. The authors have been active in developing these tools and present the state of the art and the open problems in the field. The earlier chapters provide an introduction to the subject, while the later chapters are written with graduate students and researchers in mathematical statistics in mind. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.

This Study Guide accompanies Statistics for Business and Financial Economics, 3rd Ed. (Springer, 2013), which is the most definitive Business Statistics book to use Finance, Economics, and Accounting data throughout the entire book. The Study Guide contains unique chapter reviews for each chapter in the textbook, formulas, examples and additional exercises to enhance topics and their application. Solutions are included so students can evaluate their own understanding of the material. With more real-life data sets than the other books on the market, this study guide and the textbook that it accompanies, give readers all the tools they need to learn material in class and on their own. It is immediately applicable to facing uncertainty and the science of good decision making in financial analysis, econometrics, auditing, production and operations, and marketing research. Data that is analyzed may be collected by companies in the course of their business or by governmental agencies. Students in business degree programs will find this material particularly useful to their other courses and future work.

The case-control approach is a powerful method for investigating factors that may explain a particular event. It is extensively used in epidemiology to study disease incidence, one of the best-known examples being Bradford Hill and Doll's investigation of the possible connection between cigarette smoking and lung cancer. More recently, case-control studies have been increasingly used in other fields, including sociology and econometrics. With a particular focus on statistical analysis, this book is ideal for applied and theoretical statisticians wanting an up-to-date introduction to the field. It covers the fundamentals of case-control study design and analysis as well as more recent developments, including two-stage studies, case-only studies and methods for case-control sampling in time. The latter have important applications in large prospective cohorts which require case-control sampling designs to make efficient use of resources. More theoretical background is provided in an appendix for those new to the field.

Human Development Indices and Indicators: 2018 Statistical Update is being released to ensure consistency in reporting on key human development indices and statistics. It provides a brief overview of the state of human development - snapshots of current conditions as well as long-term trends in human development indicators. It includes a full statistical annex of human development composite indices and indicators across their various dimensions. This update includes the 2017 values and ranking for the HDI and other composite indices as well as current statistics in key areas of human development for use by policymakers, researchers and others in their analytical, planning and policy work. In addition to the standard HDR tables, statistical dashboards are included to draw attention to the relationship between human well-being and five topics: quality of human development, life-course gender gaps, women's empowerment, environmental sustainability and socioeconomic sustainability. Accompanying the statistical annex is an overview of trends in human development, highlighting the considerable progress, but also the persistent deprivations and disparities

Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors' research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.

Random set theory is a fascinating branch of mathematics that amalgamates techniques from topology, convex geometry, and probability theory. Social scientists routinely conduct empirical work with data and modelling assumptions that reveal a set to which the parameter of interest belongs, but not its exact value. Random set theory provides a coherent mathematical framework to conduct identification analysis and statistical inference in this setting and has become a fundamental tool in econometrics and finance. This is the first book dedicated to the use of the theory in econometrics, written to be accessible for readers without a background in pure mathematics. Molchanov and Molinari define the basics of the theory and illustrate the mathematical concepts by their application in the analysis of econometric models. The book includes sets of exercises to accompany each chapter as well as examples to help readers apply the theory effectively.

This 2004 volume offers a broad overview of developments in the theory and applications of state space modeling. With fourteen chapters from twenty-three contributors, it offers a unique synthesis of state space methods and unobserved component models that are important in a wide range of subjects, including economics, finance, environmental science, medicine and engineering. The book is divided into four sections: introductory papers, testing, Bayesian inference and the bootstrap, and applications. It will give those unfamiliar with state space models a flavour of the work being carried out as well as providing experts with valuable state of the art summaries of different topics. Offering a useful reference for all, this accessible volume makes a significant contribution to the literature of this discipline.

Price and quantity indices are important, much-used measuring instruments, and it is therefore necessary to have a good understanding of their properties. When it was published, this book is the first comprehensive text on index number theory since Irving Fisher's 1922 The Making of Index Numbers. The book covers intertemporal and interspatial comparisons; ratio- and difference-type measures; discrete and continuous time environments; and upper- and lower-level indices. Guided by economic insights, this book develops the instrumental or axiomatic approach. There is no role for behavioural assumptions. In addition to subject matter chapters, two entire chapters are devoted to the rich history of the subject.

Over the last thirty years there has been extensive use of continuous time econometric methods in macroeconomic modelling. This monograph presents a continuous time macroeconometric model of the United Kingdom incorporating stochastic trends. Its development represents a major step forward in continuous time macroeconomic modelling. The book describes the model in detail and, like earlier models, it is designed in such a way as to permit a rigorous mathematical analysis of its steady-state and stability properties, thus providing a valuable check on the capacity of the model to generate plausible long-run behaviour. The model is estimated using newly developed exact Gaussian estimation methods for continuous time econometric models incorporating unobservable stochastic trends. The book also includes discussion of the application of the model to dynamic analysis and forecasting.

This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family.