In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters. Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy. Contains numerous exercises at the end of each chapter many of which use R and WinBUGS. Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians. About the author Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.

THE GUIDE FOR ANYONE AFRAID TO LEARN STATISTICS & ANALYTICS UPDATED WITH NEW EXAMPLES & EXERCISES This book discusses statistics and analytics using plain language and avoiding mathematical jargon. If you thought you couldn't learn these data analysis subjects because they were too technical or too mathematical, this book is for you! This edition delivers more everyday examples and end-of-chapter exercises and contains updated instructions for using Microsoft Excel. You'll use downloadable data sets and spreadsheet solutions, template-based solutions you can put right to work. Using this book, you will understand the important concepts of statistics and analytics, including learning the basic vocabulary of these subjects. Create tabular and visual summaries and learn to avoid common charting errors Gain experience working with common descriptive statistics measures including the mean, median, and mode; and standard deviation and variance, among others Understand the probability concepts that underlie inferential statistics Learn how to apply hypothesis tests, using Z, t, chi-square, ANOVA, and other techniques Develop skills using regression analysis, the most commonly-used Inferential statistical method Explore results produced by predictive analytics software Choose the right statistical or analytic techniques for any data analysis task Optionally, read the "Equation Blackboards," designed for readers who want to learn about the mathematical foundations of selected methods

Operation Research methods are often used in every field of modern life like industry, economy and medicine. The authors have compiled of the latest advancements in these methods in this volume comprising some of what is considered the best collection of these new approaches. These can be counted as a direct shortcut to what you may search for. This book provides useful applications of the new developments in OR written by leading scientists from some international universities. Another volume about exciting applications of Operations Research is planned in the near future. We hope you enjoy and benefit from this series!

In the theory and practice of econometrics the model, the method and the data are all interdependent links in information recovery-estimation and inference. Seldom, however, are the economic and statistical models correctly specified, the data complete or capable of being replicated, the estimation rules ‘optimal’ and the inferences free of distortion. Faced with these problems, Maximum Entropy Economeirics provides a new basis for learning from economic and statistical models that may be non-regular in the sense that they are ill-posed or underdetermined and the data are partial or incomplete. By extending the maximum entropy formalisms used in the physical sciences, the authors present a new set of generalized entropy techniques designed to recover information about economic systems. The authors compare the generalized entropy techniques with the performance of the relevant traditional methods of information recovery and clearly demonstrate theories with applications including

• Pure inverse problems that include first order Markov processes, and input-output, multisectoral or SAM models to
• Inverse problems with noise that include statistical models subject to ill-conditioning, non-normal errors, heteroskedasticity, autocorrelation, censored, multinomial and simultaneous response data, as well as model selection and non-stationary and dynamic control problems

The Super Bowl is the most watched sporting event in the United States. But what does participating in this event mean for the players, the halftime performers, and the cities who host the games? Is there an economic benefit from being a part of the Super Bowl and if so, how much? This Palgrave Pivot examines the economic consequences for those who participate in the Super Bowl. The book fills in gaps in the literature by examining the benefits and costs of being involved in the game. Previously, the literature has largely ignored the affect the game has had on the careers of the players, particularly the stars of the game. The economic benefit of being the halftime performer has not been considered in the literature at all. While there have been past studies about the economic impact on the cities who host of the game, this book will expand on previous research and update it with new data.

This conference proceedings volume presents advanced methods in time series estimation models that are applicable various areas of applied economic research such as international economics, macroeconomics, microeconomics, finance economics and agricultural economics. Featuring contributions presented at the 2018 International Conference on Applied Economics (ICOAE) held in Warsaw, Poland, this book presents contemporary research using applied econometric method for analysis as well as country specific studies with potential implications on economic policy. Applied economics is a rapidly growing field of economics that combines economic theory with econometrics to analyse economic problems of the real world usually with economic policy interest. ICOAE is an annual conference started in 2008 with the aim to bring together economists from different fields of applied economic research in order to share methods and ideas. Approximately 150 papers are submitted each year from about 40 countries around the world. The goal of the conference and the enclosed papers is to allow for an exchange of experiences with different applied econometric methods and to promote joint initiatives among well-established economic fields such as finance, agricultural economics, health economics, education economics, international trade theory and management and marketing strategies. Featuring global contributions, this book will be of interest to researchers, academics, professionals and policy makers in the field of applied economics and econometrics.

Modelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s. Several developments in econometrics then led to an overhaul of the techniques used to extract trends and cycles from time series. In this second edition, Terence Mills expands on the research in the area of trends and cycles over the last (almost) two decades, to highlight to students and researchers the variety of techniques and the considerations that underpin their choice for modelling trends and cycles.

Appropriate for one or two term courses in introductory Business Statistics. With Statistics for Management, Levin and Rubin have provided a non-intimidating business statistics textbook that students can easily read and understand. Like its predecessors, the Seventh Edition includes the absolute minimum of mathematical/statistical notation necessary to teach the material. Concepts are fully explained in simple, easy-to-understand language as they are presented, making the text an excellent source from which to learn and teach. After each discussion, readers are guided through real-world examples to show how textbook principles work in professional practice.

Business Statistics narrows the gap between theory and practice by focusing on relevant statistical methods, thus empowering business students to make good, data-driven decisions. Using the latest GAISE (Guidelines for Assessment and Instruction in Statistics Education) report, which included extensive revisions to reflect both the evolution of technology and new wisdom on statistics education, this edition brings a modern edge to teaching business statistics. This includes a focus on the report's key recommendations: teaching statistical thinking, focusing on conceptual understanding, integrating real data with a context and a purpose, fostering active learning, using technology to explore concepts and analyse data, and using assessments to improve and evaluate student learning. By presenting statistics in the context of real-world businesses and by emphasising analysis and understanding over computation, this book helps students be more analytical, prepares them to make better business decisions, and shows them how to effectively communicate results. Samples Preview the detailed table of contents Download a sample chapter from Business Statistics, Global Edition, 4th Edition

Welcome to Economics Express - a series of short books to help you: * take exams with confidence * prepare and deliver successful assignments * understand quickly * revise and prepare effectively. As you embark on your economic journey, this series of books will be your helpful companion. They are not meant to replace your lectures, textbooks, seminars or any other sources suggested by your lecturers. Rather, as you come to an exam or an assignment, they will help you to revise and prepare effectively. Whatever form your assessment might take, each book in the series will help you to build up the skills and knowledge you will need to maximise your performance. Each topic-based chapter will outline the key information and analysis, provide sample questions with responses, and give you the assessment advice and exam tips you will need to produce effective assessments based on these core topics. A companion website provides supporting resources for self testing, assessment, exam practice and answers to questions in the book. Ian Jacques was formerly a senior lecturer at Coventry University. He has considerable experience teaching mathematical methods to students studying economics, business and accounting.

A thrilling behind-the-scenes exploration of how governments past and present have been led astray by bad data - and why it is so hard to measure things and to do it well.

Our politicians make vital decisions and declarations every day that rely on official data. But should all statistics be trusted? In BAD DATA, House of Commons Library statistician Georgina Sturge draws back the curtain on how governments of the past and present have been led astray by figures littered with inconsistency, guesswork and uncertainty.

Discover how a Hungarian businessman's bright idea caused half a million people to go missing from UK migration statistics. Find out why it's possible for two politicians to disagree over whether poverty has gone up or down, using the same official numbers, and for both to be right at the same time. And hear about how policies like ID cards, super-casinos and stopping ex-convicts from reoffending failed to live up to their promise because they were based on shaky data.

With stories that range from the troubling to the empowering to the downright absurd, BAD DATA reveals secrets from the usually closed-off world of policy-making. It also suggests how - once we understand the human story behind the numbers - we can make more informed choices about who to trust, and when.

This book addresses the functioning of financial markets, in particular the financial market model, and modelling. More specifically, the book provides a model of adaptive preference in the financial market, rather than the model of the adaptive financial market, which is mostly based on Popper's objective propensity for the singular, i.e., unrepeatable, event. As a result, the concept of preference, following Simon's theory of satisficing, is developed in a logical way with the goal of supplying a foundation for a robust theory of adaptive preference in financial market behavior. The book offers new insights into financial market logic, and psychology: 1) advocating for the priority of behavior over information - in opposition to traditional financial market theories; 2) constructing the processes of (co)evolution adaptive preference-financial market using the concept of fetal reaction norms - between financial market and adaptive preference; 3) presenting a new typology of information in the financial market, aimed at proving point (1) above, as well as edifying an explicative mechanism of the evolutionary nature and behavior of the (real) financial market; 4) presenting sufficient, and necessary, principles or assumptions for developing a theory of adaptive preference in the financial market; and 5) proposing a new interpretation of the pair genotype-phenotype in the financial market model. The book's distinguishing feature is its research method, which is mainly logically rather than historically or empirically based. As a result, the book is targeted at generating debate about the best and most scientifically beneficial method of approaching, analyzing, and modelling financial markets.

This book covers all the topics found in introductory descriptive statistics courses, including simple linear regression and time series analysis, the fundamentals of inferential statistics (probability theory, random sampling and estimation theory), and inferential statistics itself (confidence intervals, testing). Each chapter starts with the necessary theoretical background, which is followed by a variety of examples. The core examples are based on the content of the respective chapter, while the advanced examples, designed to deepen students' knowledge, also draw on information and material from previous chapters. The enhanced online version helps students grasp the complexity and the practical relevance of statistical analysis through interactive examples and is suitable for undergraduate and graduate students taking their first statistics courses, as well as for undergraduate students in non-mathematical fields, e.g. economics, the social sciences etc.

Arthur Vogt has devoted a great deal of his scientific efforts to both person and work of Irving Fisher. This book, written with J nos Barta, gives an excellent impression of Fisher's great contributions to the theory of the price index on the one hand. On the other hand, it continues Fisher's work on this subject along the lines which several authors drew with respect to price index theory since Fisher's death fifty years ago.
"This is a highly instructive book on both the history and theory of measurement in economics. It is rather a rich source of interesting properties of more or less well known indices and famous men, especially Irving Fisher, than a precise mathematical text on the axiomatic foundations of indices." ("From the Foreword by Wolfgang Eichhorn)"

This book has taken form over several years as a result of a number of courses taught at the University of Pennsylvania and at Columbia University and a series of lectures I have given at the International Monetary Fund. Indeed, I began writing down my notes systematically during the academic year 1972-1973 while at the University of California, Los Angeles. The diverse character of the audience, as well as my own conception of what an introductory and often terminal acquaintance with formal econometrics ought to encompass, have determined the style and content of this volume. The selection of topics and the level of discourse give sufficient variety so that the book can serve as the basis for several types of courses. As an example, a relatively elementary one-semester course can be based on Chapters one through five, omitting the appendices to these chapters and a few sections in some of the chapters so indicated. This would acquaint the student with the basic theory of the general linear model, some of the prob lems often encountered in empirical research, and some proposed solutions. For such a course, I should also recommend a brief excursion into Chapter seven (logit and pro bit analysis) in view of the increasing availability of data sets for which this type of analysis is more suitable than that based on the general linear model."

Volume 39A of Research in the History of Economic Thought and Methodology features a selection of essays presented at the 2019 Conference of the Latin American Society for the History of Economic Thought (ALAHPE), edited by Felipe Almeida and Carlos Eduardo Suprinyak, as well as a new general-research essay by Daniel Kuehn, an archival discovery by Katia Caldari and Luca Fiorito, and a book review by John Hall.

Originally published in 1939, this book forms the second part of a two-volume series on the mathematics required for the examinations of the Institute of Actuaries, focusing on finite differences, probability and elementary statistics. 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.

Dynamic Programming in Economics is an outgrowth of a course intended for students in the first year PhD program and for researchers in Macroeconomics Dynamics. It can be used by students and researchers in Mathematics as well as in Economics. The purpose of Dynamic Programming in Economics is twofold: (a) to provide a rigorous, but not too complicated, treatment of optimal growth models in infinite discrete time horizon, (b) to train the reader to the use of optimal growth models and hence to help him to go further in his research. We are convinced that there is a place for a book which stays somewhere between the "minimum tool kit" and specialized monographs leading to the frontiers of research on optimal growth.

The emergence of new firm-level data, including the European Community Innovation Survey (CIS), has led to a surge of studies on innovation and firm behaviour. This book documents progress in four interrelated fields: investigation of the use of new indicators of innovation output; investigation of determinants of innovative behavior; the role of spillovers, the public knowledge infrastructure and research and development collaboration; and the impact of innovation on firm performance. Written by an international group of contributors, the studies are based on agriculture and the manufacturing and service industries in Europe and Canada and provide new insights into the driving forces behind innovation.

This is the first book to investigate individual's pessimistic and optimistic prospects for the future and their economic consequences based on sound mathematical foundations. The book focuses on fundamental uncertainty called Knightian uncertainty, where the probability distribution governing uncertainty is unknown, and it provides the reader with methods to formulate how pessimism and optimism act in an economy in a strict and unified way. After presenting decision-theoretic foundations for prudent behaviors under Knightian uncertainty, the book applies these ideas to economic models that include portfolio inertia, indeterminacy of equilibria in the Arrow-Debreu economy and in a stochastic overlapping-generations economy, learning, dynamic asset-pricing models, search, real options, and liquidity preferences. The book then proceeds to characterizations of pessimistic ( -contaminated) and optimistic ( -exuberant) behaviors under Knightian uncertainty and people's inherent pessimism (surprise aversion) and optimism (surprise loving). Those characterizations are shown to be useful in understanding several observed behaviors in the global financial crisis and in its aftermath. The book is highly recommended not only to researchers who wish to understand the mechanism of how pessimism and optimism affect economic phenomena, but also to policy makers contemplating effective economic policies whose success delicately hinges upon people's mindsets in the market. Kiyohiko Nishimura is Professor at the National Graduate Institute for Policy Studies (GRIPS) and Professor Emeritus and Distinguished Project Research Fellow of the Center for Advanced Research in Finance at The University of Tokyo. Hiroyuki Ozaki is Professor of Economics at Keio University.

This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models. It demonstrates how to take publically available data and manipulate, implement models and generate outputs typical for particular analyses. A wide spectrum of timely and practical issues in financial modelling are covered including return and risk measurement, portfolio management, option pricing and fixed income analysis. This new edition updates and expands upon the existing material providing updated examples and new chapters on equities, simulation and trading strategies, including machine learnings techniques. Select data sets are available online.

Mastering the basic concepts of mathematics is the key to understanding other subjects such as Economics, Finance, Statistics, and Accounting. Mathematics for Finance, Business and Economics is written informally for easy comprehension. Unlike traditional textbooks it provides a combination of explanations, exploration and real-life applications of major concepts.

Mathematics for Finance, Business and Economics discusses elementary mathematical operations, linear and non-linear functions and equations, differentiation and optimization, economic functions, summation, percentages and interest, arithmetic and geometric series, present and future values of annuities, matrices and Markov chains.

Aided by the discussion of real-world problems and solutions, students across the business and economics disciplines will find this textbook perfect for gaining an understanding of a core plank of their studies.

A variety of different social, natural and technological systems can be described by the same mathematical framework. This holds from Internet to the Food Webs and to the connections between different company boards given by common directors. In all these situations a graph of the elements and their connections displays a universal feature of some few elements with many connections and many with few. This book reports the experimental evidence of these Scale-free networks'' and provides to students and researchers a corpus of theoretical results and algorithms to analyse and understand these features. The contents of this book and their exposition makes it a clear textbook for the beginners and a reference book for the experts.

These proceedings highlight research on the latest trends and methods in experimental and behavioral economics. Featuring contributions presented at the 2017 Computational Methods in Experimental Economics (CMEE) conference, which was held in Lublin, Poland, it merges findings from various domains to present deep insights into topics such as game theory, decision theory, cognitive neuroscience and artificial intelligence. The fields of experimental economics and behavioral economics are rapidly evolving. Modern applications of experimental economics require the integration of know-how from disciplines including economics, computer science, psychology and neuroscience. The use of computer technology enhances researchers' ability to generate and analyze large amounts of data, allowing them to use non-standard methods of data logging for experiments such as cognitive neuronal methods. Experiments are currently being conducted with software that, on the one hand, provides interaction with the people involved in experiments, and on the other helps to accurately record their responses. The goal of the CMEE conference and the papers presented here is to provide the scientific community with essential research on and applications of computer methods in experimental economics. Combining theories, methods and regional case studies, the book offers a valuable resource for all researchers, scholars and policymakers in the areas of experimental and behavioral economics.