This book presents a critical review of the empirical literature that studies the efficiency of the forward and futures markets for foreign exchange. It provides a useful foundation for research in developing quantitative measures of risk and expected return in international finance.

For courses in Business Statistics. A classic text for accuracy and statistical precision Statistics for Business and Economics enables students to conduct serious analysis of applied problems rather than running simple "canned" applications. This text is also at a mathematically higher level than most business statistics texts and provides students with the knowledge they need to become stronger analysts for future managerial positions. In this regard, it emphasises an understanding of the assumptions that are necessary for professional analysis. In particular, it has greatly expanded the number of applications that utilise data from applied policy and research settings. The 9th Edition of this book has been revised and updated to provide students with improved problem contexts for learning how statistical methods can improve their analysis and understanding of business and economics. This revision recognises the globalisation of statistical study and in particular the global market for this book.

This book contains the most complete set of the Chinese national income and its components based on system of national accounts. It points out some fundamental issues concerning the estimation of China's national income and it is intended to the students of the field of China study around the world.

Collecting and analyzing data on unemployment, inflation, and inequality help describe the complex world around us. When published by the government, such data are called official statistics. They are reported by the media, used by politicians to lend weight to their arguments, and by economic commentators to opine about the state of society. Despite such widescale use, explanations about how these measures are constructed are seldom provided for a non-technical reader. This Measuring Society book is a short, accessible guide to six topics: jobs, house prices, inequality, prices for goods and services, poverty, and deprivation. Each relates to concepts we use on a personal level to form an understanding of the society in which we live: We need a job, a place to live, and food to eat. Using data from the United States, we answer three basic questions: why, how, and for whom these statistics have been constructed. We add some context and flavor by discussing the historical background. This book provides the reader with a good grasp of these measures. Chaitra H. Nagaraja is an Associate Professor of Statistics at the Gabelli School of Business at Fordham University in New York. Her research interests include house price indices and inequality measurement. Prior to Fordham, Dr. Nagaraja was a researcher at the U.S. Census Bureau. While there, she worked on projects relating to the American Community Survey.

Originally published in 1970; with a second edition in 1989. Empirical Bayes methods use some of the apparatus of the pure Bayes approach, but an actual prior distribution is assumed to generate the data sequence. It can be estimated thus producing empirical Bayes estimates or decision rules. In this second edition, details are provided of the derivation and the performance of empirical Bayes rules for a variety of special models. Attention is given to the problem of assessing the goodness of an empirical Bayes estimator for a given set of prior data. Chapters also focus on alternatives to the empirical Bayes approach and actual applications of empirical Bayes methods.

Originally published in 1984. This book brings together a reasonably complete set of results regarding the use of Constraint Item estimation procedures under the assumption of accurate specification. The analysis covers the case of all explanatory variables being non-stochastic as well as the case of identified simultaneous equations, with error terms known and unknown. Particular emphasis is given to the derivation of criteria for choosing the Constraint Item. Part 1 looks at the best CI estimators and Part 2 examines equation by equation estimation, considering forecasting accuracy.

Originally published in 1960 and 1966. This is an elementary introduction to the sources of economic statistics and their uses in answering economic questions. No mathematical knowledge is assumed, and no mathematical symbols are used. The book shows - by asking and answering a number of typical questions of applied economics - what the most useful statistics are, where they are found, and how they are to be interpreted and presented. The reader is introduced to the major British, European and American official sources, to the social accounts, to index numbers and averaging, and to elementary aids to inspection such as moving averages and scatter diagrams.

Originally published in 1929. This balanced combination of fieldwork, statistical measurement, and realistic applications shows a synthesis of economics and political science in a conception of an organic relationship between the two sciences that involves functional analysis, institutional interpretation, and a more workmanlike approach to questions of organization such as division of labour and the control of industry. The treatise applies the test of fact through statistical analysis to economic and political theories for the quantitative and institutional approach in solving social and industrial problems. It constructs a framework of concepts, combining both economic and political theory, to systematically produce an original statement in general terms of the principles and methods for statistical fieldwork. The separation into Parts allows selective reading for the methods of statistical measurement; the principles and fallacies of applying these measures to economic and political fields; and the resultant construction of a statistical economics and politics. Basic statistical concepts are described for application, with each method of statistical measurement illustrated with instances relevant to the economic and political theory discussed and a statistical glossary is included.

The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area. The very size and continuing growth of this need means that there is still space for new program development. Schools wishing to pursue business analytics programs intentionally assess the maturity level of their programs and take steps to close the gap. Teaching Data Analytics: Pedagogy and Program Design is a reference for faculty and administrators seeking direction about adding or enhancing analytics offerings at their institutions. It provides guidance by examining best practices from the perspectives of faculty and practitioners. By emphasizing the connection of data analytics to organizational success, it reviews the position of analytics and decision science programs in higher education, and to review the critical connection between this area of study and career opportunities. The book features: A variety of perspectives ranging from the scholarly theoretical to the practitioner applied An in-depth look into a wide breadth of skills from closely technology-focused to robustly soft human connection skills Resources for existing faculty to acquire and maintain additional analytics-relevant skills that can enrich their current course offerings. Acknowledging the dichotomy between data analytics and data science, this book emphasizes data analytics rather than data science, although the book does touch upon the data science realm. Starting with industry perspectives, the book covers the applied world of data analytics, covering necessary skills and applications, as well as developing compelling visualizations. It then dives into pedagogical and program design approaches in data analytics education and concludes with ideas for program design tactics. This reference is a launching point for discussions about how to connect industry's need for skilled data analysts to higher education's need to design a rigorous curriculum that promotes student critical thinking, communication, and ethical skills. It also provides insight into adding new elements to existing data analytics courses and for taking the next step in adding data analytics offerings, whether it be incorporating additional analytics assignments into existing courses, offering one course designed for undergraduates, or an integrated program designed for graduate students.

Key Topics in Clinical Research aims to provide a short, clear, highlighted reference to guide trainees and trainers through research and audit projects, from first idea, through to data collection and statistical analysis, to presentation and publication. This book is also designed to assist trainees in preparing for their specialty examinations by providing comprehensive, concise, easily accessible and easily understandable information on all aspects of clinical research and audit.

Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods. The social scientist's toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book's coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance. Features of this book include: Numerous worked examples with replicable R code Explicit comprehensive coverage of data assumptions Adaptation of factor methods to binary, ordinal, and categorical data Residual and outlier analysis Visualization of factor results Final chapters that treat integration of factor analysis with neural network and time series methods Presented in color with R code and introduction to R and RStudio, this book will be suitable for graduate-level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses.

Agricultural Statistics is published each year to meet the diverse need for a reliable reference book on agricultural production, supplies, consumption, facilities, costs, and returns. Its tables of annual data cover a wide variety of facts in forms suited to most common use. The estimates for crops, livestock, and poultry made by the U.S. Department of Agriculture are prepared mainly to give timely current state and national totals and averages. They are based on data obtained by sample surveys of farmers and of people who do business with farmers. The survey data are supplemented by information from the Census of Agriculture taken every five years. Being estimates, they are subject to revision as more data become available from commercial or government sources. Unless otherwise indicated, the totals for the United States shown in the various tables on area, production, numbers, price, value, supplies, and disposition are based on official Department estimates. They exclude states for which no official estimates are compiled. Extensive data includes statistics for the following: Grain and Feed Cotton, Tobacco, Sugar Crops, and Honey Oilseeds, Fats, and Oils Vegetables and Melons Hay, Seeds, and Minor Field Crops Cattle, Hogs, and Sheep Dairy and Poultry Insurance, Credit & Cooperatives Agricultural Conservation & Forestry Consumption & Family Living Fertilizers & Pesticides Miscellaneous Agricultural Statistics such as Foreign Agricultural Trade Statistics including exports, fisheries and more. Professionals in the following fields to include farmers, ranchers, soil conservationists, surveyors, agricultural economist consultants, livestock manufacturers, livestock feedlot operators, food distributors, animal scientists, food chemists, food brokers, farm and land appraisers (and more) may have the greatest interest in this volume.

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.

The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time seriesThe return series of multiple assetsBayesian inference in finance methods

Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets.

The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

Patterns of Economic Change by State and Area: Income, Employment, and Gross Domestic Product is a special edition of Business Statistics of the United States. It presents data on personal income, employment, and gross domestic product for the United States as a whole, and by region, state, and metropolitan statistical area (MSA). Data on personal income and employment extends back to 1960 for the states and regions and to 1970 for the MSAs. Patterns of Economic Change complements other Bernan Press titles such as the State and Metropolitan Area Data Book and County and City Extra. In contrast to their predominantly current and detailed cross-section data on states and metropolitan areas, this book contributes historical time-series measurements of key aggregates that show how the economies of regions, states, and metropolitan areas have responded over time to cyclical currents and long-term trends. Statistics at the state level provide a framework for analyzing current economic conditions in each state and can serve as a basis for decision making. For example: ·Federal government agencies use the statistics as a basis for allocating funds and determining matching grants to states. The statistics are also used in forecasting models to project energy and water use. ·State governments use the statistics to project tax revenues and the need for public services. ·Academic regional economists use the statistics for applied research. ·Businesses, trade associations, and labor organizations use the statistics for market research.

Have configurations of labour-management practices become embedded in the British economy? Did the dramatic decline in trade union representation in the 1980s continue throughout the 1990s, leaving more employees without a voice? Were the vestiges of union organization at the workplace a hollow shell? These and other contemporary issues of employee relations are addressed in this report. The book reports the results from the series of workplace surveys conducted by the Department of Trade and Industry, the Economic and Social Research Council, The Advisory Conciliation and Arbitration Service, and the Policy Studies Institute. Its focus is on change, captured by gathering together the enormous bank of data from all four of the large-scale and highly respected surveys, and plotting trends from 1980 to 1999. In addition, a special panel of workplaces, surveyed in both 1990 and 1998, reveals the complex processes of change.;Comprehensive in scope, the results are statistically reliable and reveal the nature and extent of change in all bar the smallest British workplaces.

Have configurations of labour-management practices become embedded in the British economy? Did the dramatic decline in trade union representation in the 1980s continue throughout the 1990s, leaving more employees without a voice? Were the vestiges of union organization at the workplace a hollow shell? These and other contemporary issues of employee relations are addressed in this report. The book reports the results from the series of workplace surveys conducted by the Department of Trade and Industry, the Economic and Social Research Council, The Advisory Conciliation and Arbitration Service, and the Policy Studies Institute. Its focus is on change, captured by gathering together the enormous bank of data from all four of the large-scale and highly respected surveys, and plotting trends from 1980 to 1999. In addition, a special panel of workplaces, surveyed in both 1990 and 1998, reveals the complex processes of change.;Comprehensive in scope, the results are statistically reliable and reveal the nature and extent of change in all bar the smallest British workplaces.

Human behavior often violates the predictions of rational choice theory. This realization has caused many social psychologists and experimental economists to attempt to develop an experimentally-based variant of game theory as an alternative descriptive model. The impetus for this book is the interest in the development of such a theory that combines elements from both disciplines and appeals to both.
The editors have brought together leading researchers in the fields of experimental economics, behavioral game theory, and social dilemmas to engage in constructive dialogue across disciplinary boundaries. This book offers a comprehensive overview of the new insights into the motivation of human behavior under a variety of naturally or artificially induced incentive structures that are emerging from their work. Amnon Rapoport--a pioneer and leader in experimental study and quantitative modeling of human decisions in social and interactive contexts--is honored.

This is a classical reprint edition of the original 1971 edition of An Introduction to Bayesian Inference in Economics. This historical volume is an early introduction to Bayesian inference and methodology which still has lasting value for today's statistician and student. The coverage ranges from the fundamental concepts and operations of Bayesian inference to analysis of applications in specific econometric problems and the testing of hypotheses and models.

Business students need the ability to think statistically about how to deal with uncertainty and its effect on decision-making in business and management. Traditional statistics courses and textbooks tend to focus on probability, mathematical detail, and heavy computation, and thus fail to meet the needs of future managers. Statistical Thinking in Business, Second Edition responds to the growing recognition that we must change the way business statistics is taught. It shows how statistics is important in all aspects of business and equips students with the skills they need to make sensible use of data and other information. The authors take an interactive, scenario-based approach and use almost no mathematical formulas, opting to use Excel for the technical work. This allows them to focus on using statistics to aid decision-making rather than how to perform routine calculations. New in the Second Edition A completely revised chapter on forecasting Re-arrangement of the material on data presentation with the inclusion of histograms and cumulative line plots A more thorough discussion of the analysis of attribute data Coverage of variable selection and model building in multiple regression End-of-chapter summaries More end-of-chapter problems A variety of case studies throughout the book The second edition also comes with a wealth of ancillary materials provided on downloadable resources packaged with the book. These include automatically-marked multiple-choice questions, answers to questions in the text, data sets, Excel experiments and demonstrations, an introduction to Excel, and the StiBstat Add-In for stem and leaf plots, box plots, distribution plots, control charts and summary statistics.

This work examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series.

The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.

Business Statistics: A Decision Making Approach provides students with an introduction to business statistics and to the analysis skills and techniques needed to make successful real-world business decisions. Written for students of all mathematical skill levels, the authors present concepts in a systematic and ordered way, drawing from their own experience as educators and consultants. Rooted in the theme that data are the starting point, Business Statistics champions the need to use and understand different types of data and data sources to be effective decision makers. This new edition integrates Microsoft Excel throughout as a way to work with statistical concepts and give students a resource that can be used in both their academic and professional careers.

Originally published in 1984. This book brings together a reasonably complete set of results regarding the use of Constraint Item estimation procedures under the assumption of accurate specification. The analysis covers the case of all explanatory variables being non-stochastic as well as the case of identified simultaneous equations, with error terms known and unknown. Particular emphasis is given to the derivation of criteria for choosing the Constraint Item. Part 1 looks at the best CI estimators and Part 2 examines equation by equation estimation, considering forecasting accuracy.

Originally published in 1987. This collection of original papers deals with various issues of specification in the context of the linear statistical model. The volume honours the early econometric work of Donald Cochrane, late Dean of Economics and Politics at Monash University in Australia. The chapters focus on problems associated with autocorrelation of the error term in the linear regression model and include appraisals of early work on this topic by Cochrane and Orcutt. The book includes an extensive survey of autocorrelation tests; some exact finite-sample tests; and some issues in preliminary test estimation. A wide range of other specification issues is discussed, including the implications of random regressors for Bayesian prediction; modelling with joint conditional probability functions; and results from duality theory. There is a major survey chapter dealing with specification tests for non-nested models, and some of the applications discussed by the contributors deal with the British National Accounts and with Australian financial and housing markets.