"A book perfect for this moment" -Katherine M. O'Regan, Former Assistant Secretary, US Department of Housing and Urban Development More than fifty years after the passage of the Fair Housing Act, American cities remain divided along the very same lines that this landmark legislation explicitly outlawed. Keeping Races in Their Places tells the story of these lines-who drew them, why they drew them, where they drew them, and how they continue to circumscribe residents' opportunities to this very day. Weaving together sophisticated statistical analyses of more than a century's worth of data with an engaging, accessible narrative that brings the numbers to life, Keeping Races in Their Places exposes the entrenched effects of redlining on American communities. This one-of-a-kind contribution to the real estate and urban economics literature applies the author's original geographic information systems analyses to historical maps to reveal redlining's causal role in shaping today's cities. Spanning the era from the Great Migration to the Great Recession, Keeping Races in Their Places uncovers the roots of the Black-white wealth gap, the subprime lending crisis, and today's lack of affordable housing in maps created by banks nearly a century ago. Most of all, it offers hope that with the latest scholarly tools we can pinpoint how things went wrong-and what we must do to make them right.

"A book perfect for this moment" -Katherine M. O'Regan, Former Assistant Secretary, US Department of Housing and Urban Development More than fifty years after the passage of the Fair Housing Act, American cities remain divided along the very same lines that this landmark legislation explicitly outlawed. Keeping Races in Their Places tells the story of these lines-who drew them, why they drew them, where they drew them, and how they continue to circumscribe residents' opportunities to this very day. Weaving together sophisticated statistical analyses of more than a century's worth of data with an engaging, accessible narrative that brings the numbers to life, Keeping Races in Their Places exposes the entrenched effects of redlining on American communities. This one-of-a-kind contribution to the real estate and urban economics literature applies the author's original geographic information systems analyses to historical maps to reveal redlining's causal role in shaping today's cities. Spanning the era from the Great Migration to the Great Recession, Keeping Races in Their Places uncovers the roots of the Black-white wealth gap, the subprime lending crisis, and today's lack of affordable housing in maps created by banks nearly a century ago. Most of all, it offers hope that with the latest scholarly tools we can pinpoint how things went wrong-and what we must do to make them right.

Explains modern SDC techniques for data stewards and develop tools to implement them. Explains the logic behind modern privacy protections for researchers and how they may use publicly released data to generate valid statistical inferences-as well as the limitations imposed by SDC techniques.

The behaviour of commodity prices never ceases to marvel economists, financial analysts, industry experts, and policymakers. Unexpected swings in commodity prices used to occur infrequently but have now become a permanent feature of global commodity markets. This book is about modelling commodity price shocks. It is intended to provide insights into the theoretical, conceptual, and empirical modelling of the underlying causes of global commodity price shocks. Three main objectives motivated the writing of this book. First, to provide a variety of modelling frameworks for documenting the frequency and intensity of commodity price shocks. Second, to evaluate existing approaches used for forecasting large movements in future commodity prices. Third, to cover a wide range and aspects of global commodities including currencies, rare-hard-lustrous transition metals, agricultural commodities, energy, and health pandemics. Some attempts have already been made towards modelling commodity price shocks. However, most tend to narrowly focus on a subset of commodity markets, i.e., agricultural commodities market and/or the energy market. In this book, the author moves the needle forward by operationalizing different models, which allow researchers to identify the underlying causes and effects of commodity price shocks. Readers also learn about different commodity price forecasting models. The author presents the topics to readers assuming less prior or specialist knowledge. Thus, the book is accessible to industry analysts, researchers, undergraduate and graduate students in economics and financial economics, academic and professional economists, investors, and financial professionals working in different sectors of the commodity markets. Another advantage of the book's approach is that readers are not only exposed to several innovative modelling techniques to add to their modelling toolbox but are also exposed to diverse empirical applications of the techniques presented.

Applied data-centric social sciences aim to develop both methodology and practical applications of various fields of sciences and businesses with rich data. Specifically, in the social sciences, a vast amount of data on human activities may be useful for understanding collective human nature. In this book, the author introduces several mathematical techniques for handling a huge volume of data and analyzing collective human behavior. The book is constructed from data-oriented investigation, with mathematical methods and expressions used for dealing with data for several specific problems. The fundamental philosophy underlying the book is that both mathematical and physical concepts are determined by the purposes of data analysis. This philosophy is shown throughout exemplar studies of several fields in socio-economic systems. From a data-centric point of view, the author proposes a concept that may change people s minds and cause them to start thinking from the basis of data. Several goals underlie the chapters of the book. The first is to describe mathematical and statistical methods for data analysis, and toward that end the author delineates methods with actual data in each chapter. The second is to find a cyber-physical link between data and data-generating mechanisms, as data are always provided by some kind of data-generating process in the real world. The third goal is to provide an impetus for the concepts and methodology set forth in this book to be applied to socio-economic systems."

1. Material on single asset problems, market timing, unconditional and conditional portfolio problems, hedged portfolios. 2. Inference via both Frequentist and Bayesian paradigms. 3. A comprehensive treatment of overoptimism and overfitting of trading strategies. 4. Advice on backtesting strategies. 5. Dozens of examples and hundreds of exercises for self study.

'Overall, the book is highly technical, including full mathematical proofs of the results stated. Potential readers are post-graduate students or researchers in Quantitative Risk Management willing to have a manual with the state-of-the-art on portfolio diversification and risk aggregation with heavy tails, including the fundamental theorems as well as collateral (but most useful) results on majorization and copula theory.'Quantitative Finance This book offers a unified approach to the study of crises, large fluctuations, dependence and contagion effects in economics and finance. It covers important topics in statistical modeling and estimation, which combine the notions of copulas and heavy tails - two particularly valuable tools of today's research in economics, finance, econometrics and other fields - in order to provide a new way of thinking about such vital problems as diversification of risk and propagation of crises through financial markets due to contagion phenomena, among others. The aim is to arm today's economists with a toolbox suited for analyzing multivariate data with many outliers and with arbitrary dependence patterns. The methods and topics discussed and used in the book include, in particular, majorization theory, heavy-tailed distributions and copula functions - all applied to study robustness of economic, financial and statistical models, and estimation methods to heavy tails and dependence.

There isn't a book currently on the market which focuses on multiple hypotheses testing. - Can be used on a range of course, including social & behavioral sciences, biological sciences, as well as professional researchers. Includes various examples of the multiple hypotheses method in practice in a variety of fields, including: sport and crime.

Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is an intermediate-level statistics text that fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the authors introduce to students the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear models. In addition to adding R functionality, this second edition features three new chapters and several sections on new topics that are extremely relevant to the current research in statistical methodology. Revised or expanded topics include linear fixed, random and mixed effects models, generalized linear models, Bayesian and hierarchical linear models, model selection, multiple comparisons, and regularized and robust regression. New to the Second Edition: Coverage of inference for linear models has been expanded into two chapters. Expanded coverage of multiple comparisons, random and mixed effects models, model selection, and missing data. A new chapter on generalized linear models (Chapter 12). A new section on multivariate linear models in Chapter 13, and expanded coverage of the Bayesian linear models and longitudinal models. A new section on regularized regression in Chapter 14. Detailed data illustrations using R. The authors' fresh approach, methodical presentation, wealth of examples, use of R, and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models, generalized linear models, nonlinear models, and dynamic models.

Over the course of his professional life, John Maynard Keynes altered his views from free trade in the classical tradition to restricted trade. At the end of his career, his position on the issue was still not categorically resolved even though the evidence seems to suggest that he moved closer to a system of managed trade. In that model, nations would not leave their foreign trade interests open to the vagaries of the free market, but rather exercise some degree of control over them just as they would their domestic economies. Nevertheless, there is no general agreement among economists as to whether Keynes ended his career in the camp of the free traders or aligned himself with the protectionists. John Maynard Keynes: Free Trader or Protectionist? seeks an answer to this question by analyzing Keynes' own views on this issue, as stated in his major publications, letters, speeches, testimony before government bodies, newspaper articles, participation in conferences, and other sources. Through this detailed review of what Keynes himself had to say on the issue as opposed to what others have alleged, this book strives to make a significant contribution to the resolution of this issue.

Microsimulation Modelling involves the application of simulation methods to micro data for the purposes of evaluating the effectiveness and improving the design of public policy. The field has existed for over 50 years and has been applied to many different policy areas and is a methodology that is applied within both government and academia. This handbook brings together leading authors in the field to describe and discuss the main current issues within the field. The handbook provides an overview of current developments across each of the sub-fields of microsimulation modelling such as tax-benefit, pensions, spatial, health, labour, consumption, transport and land use policy as well as macro-micro, environmental and demographic issues. It focuses also on the modelling different micro units such as households, firms and farms. Each chapter discusses its sub-field under the following headings: the main methodologies of the sub-field; survey the literature in the area; critique the literature; and propose future directions for research within the sub-field.

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.

*Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied. *Uses numerous graphs in R to illustrate the model's results, assumptions, and other features. *Does not assume a background in calculus or linear algebra; rather, an introductory statistics course and familiarity with elementary algebra are sufficient. *Provides many examples using real world datasets relevant to various academic disciplines. *Fully integrates the R software environment in its numerous examples.

*Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied. *Uses numerous graphs in R to illustrate the model's results, assumptions, and other features. *Does not assume a background in calculus or linear algebra; rather, an introductory statistics course and familiarity with elementary algebra are sufficient. *Provides many examples using real world datasets relevant to various academic disciplines. *Fully integrates the R software environment in its numerous examples.

Much of our thinking is flawed because it is based on faulty intuition. By using the framework and tools of probability and statistics, we can overcome this to provide solutions to many real-world problems and paradoxes. We show how to do this, and find answers that are frequently very contrary to what we might expect. Along the way, we venture into diverse realms and thought experiments which challenge the way that we see the world. Features: An insightful and engaging discussion of some of the key ideas of probabilistic and statistical thinking Many classic and novel problems, paradoxes, and puzzles An exploration of some of the big questions involving the use of choice and reason in an uncertain world The application of probability, statistics, and Bayesian methods to a wide range of subjects, including economics, finance, law, and medicine Exercises, references, and links for those wishing to cross-reference or to probe further Solutions to exercises at the end of the book This book should serve as an invaluable and fascinating resource for university, college, and high school students who wish to extend their reading, as well as for teachers and lecturers who want to liven up their courses while retaining academic rigour. It will also appeal to anyone who wishes to develop skills with numbers or has an interest in the many statistical and other paradoxes that permeate our lives. Indeed, anyone studying the sciences, social sciences, or humanities on a formal or informal basis will enjoy and benefit from this book.

There isn't a book currently on the market which focuses on multiple hypotheses testing. - Can be used on a range of course, including social & behavioral sciences, biological sciences, as well as professional researchers. Includes various examples of the multiple hypotheses method in practice in a variety of fields, including: sport and crime.

The development of economics changed dramatically during the twentieth century with the emergence of econometrics, macroeconomics and a more scientific approach in general. One of the key individuals in the transformation of economics was Ragnar Frisch, professor at the University of Oslo and the first Nobel Laureate in economics in 1969. He was a co-founder of the Econometric Society in 1930 (after having coined the word econometrics in 1926) and edited the journal Econometrics for twenty-two years. The discovery of the manuscripts of a series of eight lectures given by Frisch at the Henri Poincare Institute in March-April 1933 on The Problems and Methods of Econometrics will enable economists to more fully understand his overall vision of econometrics. This book is a rare exhibition of Frisch's overview on econometrics and is published here in English for the first time. Edited and with an introduction by Olav Bjerkholt and Ariane Dupont-Kieffer, Frisch's eight lectures provide an accessible and astute discussion of econometric issues from philosophical foundations to practical procedures. Concerning the development of economics in the twentieth century and the broader visions about economic science in general and econometrics in particular held by Ragnar Frisch, this book will appeal to anyone with an interest in the history of economics and econometrics.

This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. These data-driven models seek to replace the "classical " parametric models of the past, which were rigid and often linear. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures. They provide a balanced view of new developments in the analysis and modeling of applied sciences with cross-section, time series, panel, and spatial data sets. The major topics of the volume include: the methodology of semiparametric models and special regressor methods; inverse, ill-posed, and well-posed problems; different methodologies related to additive models; sieve regression estimators, nonparametric and semiparametric regression models, and the true error of competing approximate models; support vector machines and their modeling of default probability; series estimation of stochastic processes and some of their applications in Econometrics; identification, estimation, and specification problems in a class of semilinear time series models; nonparametric and semiparametric techniques applied to nonstationary or near nonstationary variables; the estimation of a set of regression equations; and a new approach to the analysis of nonparametric models with exogenous treatment assignment.

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with small-area spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented, followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges. Robert Haining is Emeritus Professor in Human Geography, University of Cambridge, England. He is the author of Spatial Data Analysis in the Social and Environmental Sciences (1990) and Spatial Data Analysis: Theory and Practice (2003). He is a Fellow of the RGS-IBG and of the Academy of Social Sciences. Guangquan Li is Senior Lecturer in Statistics in the Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle, England. His research includes the development and application of Bayesian methods in the social and health sciences. He is a Fellow of the Royal Statistical Society.

The book provides an integrated approach to risk sharing, risk spreading and efficient regulation through principal agent models. It emphasizes the role of information asymmetry and risk sharing in contracts as an alternative to transaction cost considerations. It examines how contracting, as an institutional mechanism to conduct transactions, spreads risks while attempting consolidation. It further highlights the shifting emphasis in contracts from Coasian transaction cost saving to risk sharing and shows how it creates difficulties associated with risk spreading, and emphasizes the need for efficient regulation of contracts at various levels. Each of the chapters is structured using a principal agent model, and all chapters incorporate adverse selection (and exogenous randomness) as a result of information asymmetry, as well as moral hazard (and endogenous randomness) due to the self-interest-seeking behavior on the part of the participants.

Principles of Copula Theory explores the state of the art on copulas and provides you with the foundation to use copulas in a variety of applications. Throughout the book, historical remarks and further readings highlight active research in the field, including new results, streamlined presentations, and new proofs of old results. After covering the essentials of copula theory, the book addresses the issue of modeling dependence among components of a random vector using copulas. It then presents copulas from the point of view of measure theory, compares methods for the approximation of copulas, and discusses the Markov product for 2-copulas. The authors also examine selected families of copulas that possess appealing features from both theoretical and applied viewpoints. The book concludes with in-depth discussions on two generalizations of copulas: quasi- and semi-copulas. Although copulas are not the solution to all stochastic problems, they are an indispensable tool for understanding several problems about stochastic dependence. This book gives you the solid and formal mathematical background to apply copulas to a range of mathematical areas, such as probability, real analysis, measure theory, and algebraic structures.

Data Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship. The FAIR principles are used as a guide throughout the text, and this book should leave experimentalists consciously incompetent about data stewardship and motivated to respect data stewards as representatives of a new profession, while possibly motivating others to consider a career in the field. The ebook, avalable for no additional cost when you buy the paperback, will be updated every 6 months on average (providing that significant updates are needed or avaialble). Readers will have the opportunity to contribute material towards these updates, and to develop their own data management plans, via the free Data Stewardship Wizard.

Law and economics research has had an enormous impact on the laws of contracts, torts, property, crimes, corporations, and antitrust, as well as public regulation and fundamental rights. The Law and Economics of Patent Damages, Antitrust, and Legal Process examines several areas of important research by a variety of international scholars. It contains technical papers on the appropriate way to estimate damages in patent disputes, as well as methods for evaluating relevant markets and vertically integrated firms when determining the competitive effects of mergers and other actions. There are also papers on the implication of different legal processes, regulations, and liability rules on consumer welfare, which range from the impact of delays in legal decisions in labour cases in France to issues of criminal liability related to the use of artificial intelligence. This volume of Research in Law and Economics is a must-read for researchers and professionals of patent damages, antitrust, labour, and legal process.

Winner of the 2017 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA) A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against intelligent adversaries. Many examples throughout illustrate the application of the ARA approach to a variety of games and strategic situations. Focuses on the recent subfield of decision analysis, ARA Compares ideas from decision theory and game theory Uses multi-agent influence diagrams (MAIDs) throughout to help readers visualize complex information structures Applies the ARA approach to simultaneous games, auctions, sequential games, and defend-attack games Contains an extended case study based on a real application in railway security, which provides a blueprint for how to perform ARA in similar security situations Includes exercises at the end of most chapters, with selected solutions at the back of the book The book shows decision makers how to build Bayesian models for the strategic calculation of their opponents, enabling decision makers to maximize their expected utility or minimize their expected loss. This new approach to risk analysis asserts that analysts should use Bayesian thinking to describe their beliefs about an opponent's goals, resources, optimism, and type of strategic calculation, such as minimax and level-k thinking. Within that framework, analysts then solve the problem from the perspective of the opponent while placing subjective probability distributions on all unknown quantities. This produces a distribution over the actions of the opponent and enables analysts to maximize their expected utilities.