Most academic and policy commentary represents adverse selection as a severe problem in insurance, which should always be deprecated, avoided or minimised. This book gives a contrary view. It details the exaggeration of adverse selection in insurers' rhetoric and insurance economics, and presents evidence that in many insurance markets, adverse selection is weaker than most commentators suggest. A novel arithmetical argument shows that from a public policy perspective, 'weak' adverse selection can be a good thing. This is because a degree of adverse selection is needed to maximise 'loss coverage', the expected fraction of the population's losses which is compensated by insurance. This book will be valuable for those interested in public policy arguments about insurance and discrimination: academics (in economics, law and social policy), policymakers, actuaries, underwriters, disability activists, geneticists and other medical professionals.

Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.

Now in its fifth edition, this book offers a detailed yet concise introduction to the growing field of statistical applications in finance. The reader will learn the basic methods for evaluating option contracts, analyzing financial time series, selecting portfolios and managing risks based on realistic assumptions about market behavior. The focus is both on the fundamentals of mathematical finance and financial time series analysis, and on applications to specific problems concerning financial markets, thus making the book the ideal basis for lectures, seminars and crash courses on the topic. All numerical calculations are transparent and reproducible using quantlets. For this new edition the book has been updated and extensively revised and now includes several new aspects such as neural networks, deep learning, and crypto-currencies. Both R and Matlab code, together with the data, can be downloaded from the book's product page and the Quantlet platform. The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding Data-Driven Documents-based visualization allow readers to reproduce the tables, pictures and calculations inside this Springer book. "This book provides an excellent introduction to the tools from probability and statistics necessary to analyze financial data. Clearly written and accessible, it will be very useful to students and practitioners alike." Yacine Ait-Sahalia, Otto Hack 1903 Professor of Finance and Economics, Princeton University

"The level is appropriate for an upper-level undergraduate or graduate-level statistics major. Sampling: Design and Analysis (SDA) will also benefit a non-statistics major with a desire to understand the concepts of sampling from a finite population. A student with patience to delve into the rigor of survey statistics will gain even more from the content that SDA offers. The updates to SDA have potential to enrich traditional survey sampling classes at both the undergraduate and graduate levels. The new discussions of low response rates, non-probability surveys, and internet as a data collection mode hold particular value, as these statistical issues have become increasingly important in survey practice in recent years… I would eagerly adopt the new edition of SDA as the required textbook." (Emily Berg, Iowa State University)

What is the unemployment rate? What is the total area of land planted with soybeans? How many persons have antibodies to the virus causing COVID-19? Sampling: Design and Analysis, Third Edition shows you how to design and analyze surveys to answer these and other questions. This authoritative text, used as a standard reference by numerous survey organizations, teaches the principles of sampling with examples from social sciences, public opinion research, public health, business, agriculture, and ecology. Readers should be familiar with concepts from an introductory statistics class including probability and linear regression; optional sections contain statistical theory for readers familiar with mathematical statistics.

Key Features:

Has been thoroughly revised to incorporate recent research and applications.

Includes a new chapter on nonprobability samples, and more than 200 new examples and exercises have been added.

Teaches the principles of sampling with examples from social sciences, public opinion research, public health, business, agriculture, and ecology.

SDA’s companion website contains data sets, computer code, and links to two free downloadable supplementary books (also available in paperback) that provide step-by-step guides—with code, annotated output, and helpful tips—for working through the SDA examples. Instructors can use either R or SAS® software.

SAS® Software Companion for Sampling: Design and Analysis, Third Edition by Sharon L. Lohr (2022, CRC Press)

R Companion for Sampling: Design and Analysis, Third Edition by Yan Lu and Sharon L. Lohr (2022, CRC Press)

Table of Contents

1. Introduction 2. Simple Probability Samples 3. Stratified Sampling 4. Ratio and Regression Estimation 5. Cluster Sampling with Equal Probabilities 6. Sampling with Unequal Probabilities 7. Complex Surveys 8. Nonresponse 9. Variance Estimation in Complex Surveys 10. Categorical Data Analysis in Complex Surveys 11. Regression in Complex Surveys 12. Two-Phase Sampling 13. Estimating the Size of a Population 14. Rare Populations and Small Area Estimation 15. Nonprobability Samples 16. Survey Quality

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.

This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many 'random effects'. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write-up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to 'translate' their skills with more traditional models to a Bayesian framework, will benefit greatly from the lessons in this text.

Statistics for Business is meant as a textbook for students in business, computer science, bioengineering, environmental technology, and mathematics. In recent years, business statistics is used widely for decision making in business endeavours. It emphasizes statistical applications, statistical model building, and determining the manual solution methods. Special Features: This text is prepared based on "self-taught" method. For most of the methods, the required algorithm is clearly explained using flow-charting methodology. More than 200 solved problems provided. More than 175 end-of-chapter exercises with answers are provided. This allows teachers ample flexibility in adopting the textbook to their individual class plans. This textbook is meant to for beginners and advanced learners as a text in Statistics for Business or Applied Statistics for undergraduate and graduate students.

Davidson and MacKinnon have written an outstanding textbook for graduates in econometrics, covering both basic and advanced topics and using geometrical proofs throughout for clarity of exposition. The book offers a unified theoretical perspective, and emphasizes the practical applications of modern theory.

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.

This introductory textbook for business statistics teaches statistical analysis and research methods via business case studies and financial data using Excel, Minitab, and SAS. Every chapter in this textbook engages the reader with data of individual stock, stock indices, options, and futures. One studies and uses statistics to learn how to study, analyze, and understand a data set of particular interest. Some of the more popular statistical programs that have been developed to use statistical and computational methods to analyze data sets are SAS, SPSS, and Minitab. Of those, we look at Minitab and SAS in this textbook. One of the main reasons to use Minitab is that it is the easiest to use among the popular statistical programs. We look at SAS because it is the leading statistical package used in industry. We also utilize the much less costly and ubiquitous Microsoft Excel to do statistical analysis, as the benefits of Excel have become widely recognized in the academic world and its analytical capabilities extend to about 90 percent of statistical analysis done in the business world. We demonstrate much of our statistical analysis using Excel and double check the analysis and outcomes using Minitab and SAS-also helpful in some analytical methods not possible or practical to do in Excel.

Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming. The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses Generating general solutions using macros Customizing output Producing insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices Connecting SAS with R Covering topics that are part of both base and certification exams.

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.

Central Bank Balance Sheet and Real Business Cycles argues that a deeper comprehension of changes to the central bank balance sheet can lead to more effective policymaking. Any transaction engaged in by the central bank-issuing currency, conducting foreign exchange operations, investing its own funds, intervening to provide emergency liquidity assistance and carrying out monetary policy operations-influences its balance sheet. Despite this, many central banks throughout the world have largely ignored balance sheet movements, and have instead focused on implementing interest rates. In this book, Mustapha Abiodun Akinkunmi highlights the challenges and controversies faced by central banks in the past and present when implementing policies, and analyzes the links between these policies, the central bank balance sheet, and the consequences to economies as a whole. He argues that the composition and evolution of the central bank balance sheet provides a valuable basis for understanding the needs of an economy, and is an important tool in developing strategies that would most effectively achieve policy goals. This book is an important resource for anyone interested in monetary policy or whose work is affected by the actions of the policies of central banks.

This volume deals with two complementary topics. On one hand the book deals with the problem of determining the the probability distribution of a positive compound random variable, a problem which appears in the banking and insurance industries, in many areas of operational research and in reliability problems in the engineering sciences. On the other hand, the methodology proposed to solve such problems, which is based on an application of the maximum entropy method to invert the Laplace transform of the distributions, can be applied to many other problems. The book contains applications to a large variety of problems, including the problem of dependence of the sample data used to estimate empirically the Laplace transform of the random variable. Contents Introduction Frequency models Individual severity models Some detailed examples Some traditional approaches to the aggregation problem Laplace transforms and fractional moment problems The standard maximum entropy method Extensions of the method of maximum entropy Superresolution in maxentropic Laplace transform inversion Sample data dependence Disentangling frequencies and decompounding losses Computations using the maxentropic density Review of statistical procedures

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.

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.

This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits, they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to objective and indeed prior-free analysis for less subjectively inclined statisticians. The generous mixture of theory, illustrations, applications and exercises is suitable for statisticians at all levels of experience, as well as for data-oriented scientists. Some confidence distributions are less dispersed than their competitors. This concept leads to a theory of risk functions and comparisons for distributions of confidence. Neyman-Pearson type theorems leading to optimal confidence are developed and richly illustrated. Exact and optimal confidence distribution is the gold standard for inferred epistemic distributions. Confidence distributions and likelihood functions are intertwined, allowing prior distributions to be made part of the likelihood. Meta-analysis in likelihood terms is developed and taken beyond traditional methods, suiting it in particular to combining information across diverse data sources.

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.

This book presents the latest advances in the theory and practice of Marshall-Olkin distributions. These distributions have been increasingly applied in statistical practice in recent years, as they make it possible to describe interesting features of stochastic models like non-exchangeability, tail dependencies and the presence of a singular component. The book presents cutting-edge contributions in this research area, with a particular emphasis on financial and economic applications. It is recommended for researchers working in applied probability and statistics, as well as for practitioners interested in the use of stochastic models in economics. This volume collects selected contributions from the conference “Marshall-Olkin Distributions: Advances in Theory and Applications,� held in Bologna on October 2-3, 2013.

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.

The main objective of this book is to develop a strategy and policy measures to enhance the formalization of the shadow economy in order to improve the competitiveness of the economy and contribute to economic growth; it explores these issues with special reference to Serbia. The size and development of the shadow economy in Serbia and other Central and Eastern European countries are estimated using two different methods (the MIMIC method and household-tax-compliance method). Micro-estimates are based on a special survey of business entities in Serbia, which for the first time allows us to explore the shadow economy from the perspective of enterprises and entrepreneurs. The authors identify the types of shadow economy at work in business entities, the determinants of shadow economy participation, and the impact of competition from the informal sector on businesses. Readers will learn both about the potential fiscal effects of reducing the shadow economy to the levels observed in more developed countries and the effects that formalization of the shadow economy can have on economic growth.

Inequality is a charged topic. Measures of income inequality rose in the USA in the 1990s to levels not seen since 1929 and gave rise to a suspicion, not for the first time, of a link between radical inequality and financial instability with a resulting crisis under capitalism. Professional macroeconomists have generally taken little interest in inequality because, within the parameters of traditional economic theory, the economy will stabilize itself at full employment. In addition, enlightened economists could enact stabilizing measures to manage any imbalances. The dominant voices among academic economists were unable to interpret the causal forces at work during both the Great Depression and the recent global financial crisis. In Inequality and Instability, James K. Galbraith argues that since there has been no serious work done on the macroeconomic effects of inequality, new sources of evidence are required. Galbraith offers for the first time a vast expansion of the capacity to calculate measures of inequality both at lower and higher levels of aggregation. Instead of measuring inequality as traditionally done, by country, Galbraith insists that to understand real differences that have real effects, inequality must be examined through both smaller and larger administrative units, like sub-national levels within and between states and provinces, multinational continental economies, and the world. He points out that inequality could be captured by measures across administrative boundaries to capture data on more specific groups to which people belong. For example, in China, economic inequality reflects the difference in average income levels between city and countryside, or between coastal regions and the interior, and a simple ratio averages would be an indicator of trends in inequality over the country as a whole. In a comprehensive presentation of this new method of using data, Inequality and Instability offers an unequaled look at the US economy and various global economies that was not accessible to us before. This provides a more sophisticated and a more accurate picture of inequality around the world, and how inequality is one of the most basic sources of economic instability.

Pioneered by American economist Paul Samuelson, revealed preference theory is based on the idea that the preferences of consumers are revealed in their purchasing behavior. Researchers in this field have developed complex and sophisticated mathematical models to capture the preferences that are 'revealed' through consumer choice behavior. This study of consumer demand and behavior is closely tied up with econometrics (especially nonparametric econometrics), where testing the validity of different theoretical models is an important aspect of research. The theory of revealed preference has a very long and distinguished tradition in economics, but there was no systematic presentation of the theory until now. This book deals with basic questions in economic theory, such as the relation between theory and data, and studies the situations in which empirical observations are consistent or inconsistent with some of the best known theories in economics.

This book deals with the application of wavelet and spectral methods for the analysis of nonlinear and dynamic processes in economics and finance. It reflects some of the latest developments in the area of wavelet methods applied to economics and finance. The topics include business cycle analysis, asset prices, financial econometrics, and forecasting. An introductory paper by James Ramsey, providing a personal retrospective of a decade's research on wavelet analysis, offers an excellent overview over the field.

This book provides a detailed introduction to the theoretical and methodological foundations of production efficiency analysis using benchmarking. Two of the more popular methods of efficiency evaluation are Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA), both of which are based on the concept of a production possibility set and its frontier. Depending on the assumed objectives of the decision-making unit, a Production, Cost, or Profit Frontier is constructed from observed data on input and output quantities and prices. While SFA uses different maximum likelihood estimation techniques to estimate a parametric frontier, DEA relies on mathematical programming to create a nonparametric frontier. Yet another alternative is the Convex Nonparametric Frontier, which is based on the assumed convexity of the production possibility set and creates a piecewise linear frontier consisting of a number of tangent hyper planes. Three of the papers in this volume provide a detailed and relatively easy to follow exposition of the underlying theory from neoclassical production economics and offer step-by-step instructions on the appropriate model to apply in different contexts and how to implement them. Of particular appeal are the instructions on (i) how to write the codes for different SFA models on STATA, (ii) how to write a VBA Macro for repetitive solution of the DEA problem for each production unit on Excel Solver, and (iii) how to write the codes for the Nonparametric Convex Frontier estimation. The three other papers in the volume are primarily theoretical and will be of interest to PhD students and researchers hoping to make methodological and conceptual contributions to the field of nonparametric efficiency analysis.