This fifth edition of a classic text is appropriate for a one semester general course in Applied Statistics or as a reference book for practicing researchers in a wide variety of disciplines, including medicine, health and human services, natural and social sciences, law, and engineering. This practical book describes the Bayesian principles necessary for applied clinical research and strategic interaction, which are frequently omitted in other texts. After a comprehensive treatment of probability theory concepts, theorems, and some basic proofs, this concisely written text illustrates sampling distributions and their importance in estimation for the purpose of statistical inference. The book then shifts its focus to the essentials associated with confidence intervals and hypothesis testing for major population parameters; namely, the population mean, population variance, and population proportion. In addition, it thoroughly describes the properties of expectations and variance, the basics of correlation and simple linear regression, as well as non-parametric statistics.

The impact of globalization of financial markets is a highly debated topic, particularly in recent months when the issue of globalization and contagion of financial distress has become a focus of intense policy debate. The papers in this volume provide an up-to-date overview of the key issues in this debate. While most of the contributions were prepared after the initial outbreak of the current global turmoil and financial crisis, they identify the relative strengths of the risk diversification and risk transmission processes and examine the empirical evidence to date. The book considers the relative roles of banks, nonbank financial institutions and capital markets in both risk diversification and risk transmission. It then evaluates the current status of crisis resolution in a global context, and speculates where to go from here in terms of understanding, resolution, prevention and public policy.

This two-volume work aims to present as completely as possible the methods of statistical inference with special reference to their economic applications. The reader will find a description not only of the classical concepts and results of mathematical statistics, but also of concepts and methods recently developed for the specific needs of econometrics. The authors have sought to avoid an overly technical presentation and go to some lengths to encourage an intuitive understanding of the results by providing numerous examples throughout. The breadth of approaches and the extensive coverage of the two volumes provide for a thorough and entirely self-contained course in modern econometrics. Volume 1 provides an introduction to general concepts and methods in statistics and econometrics, and goes on to cover estimation and prediction. Volume 2 focuses on testing, confidence regions, model selection, and asymptotic theory.

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees, and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data. The ten chapters of the book fall naturally into three sections. Chapters 1 to 3 cover some basic concepts of finance, focusing on the properties of returns on an asset. Chapters 4 through 6 cover aspects of portfolio theory and the methods of estimation needed to implement that theory. The remainder of the book, Chapters 7 through 10, discusses several models for financial data, along with the implications of those models for portfolio theory and for understanding the properties of return data. The audience for the book is students majoring in Statistics and Economics as well as in quantitative fields such as Mathematics and Engineering. Readers are assumed to have some background in statistical methods along with courses in multivariate calculus and linear algebra.

Tourism demand is the foundation on which all tourism-related business decisions ultimately rest. Governments and companies such as airlines, tour operators, hotels, cruise ship lines, and recreation facility providers are interested in the demand for their products by tourists. The success of many businesses depends largely or totally on the state of tourism demand, and ultimate management failure is quite often due to the failure to meet market demand. This book introduces students, researchers and practitioners to the modern developments in advanced econometric methodology within the context of tourism demand analysis, and illustrates these developments with actual tourism applications. The concepts and computations of modern advanced econometric modelling methodologies are introduced at a level that is accessible to specialists and non-specialists alike. The methodologies introduced include general-to-specific modelling, cointegration, vector autoregression, time varying parameter modelling, panel data analysis and the almost ideal demand system (AIDS). In order to help the reader understand the various methodologies, extensive tourism demand examples are provided throughout the volume.

There is no book currently available that gives a comprehensive treatment of the design, construction, and use of index numbers. However, there is a pressing need for one in view of the increasing and more sophisticated employment of index numbers in the whole range of applied economics and specifically in discussions of macroeconomic policy. In this book, R. G. D. Allen meets this need in simple and consistent terms and with comprehensive coverage.

The text begins with an elementary survey of the index-number problem before turning to more detailed treatments of the theory and practice of index numbers. The binary case in which one time period is compared with another is first developed and illustrated with numerous examples. This is to prepare the ground for the central part of the text on runs of index numbers. Particular attention is paid both to fixed-weighted and to chain forms as used in a wide range of published index numbers taken mainly from British official sources.

This work deals with some further problems in the construction of index numbers, problems which are both troublesome and largely unresolved. These include the use of sampling techniques in index-number design and the theoretical and practical treatment of quality changes. It is also devoted to a number of detailed and specific applications of index-number techniques to problems ranging from national-income accounting, through the measurement of inequality of incomes and international comparisons of real incomes, to the use of index numbers of stock-market prices. Aimed primarily at students of economics, whatever their age and range of interests, this work will also be of use to those who handle index numbers professionally. "R. G. D. Allen" (1906-1983) was Professor Emeritus at the University of London. He was also once president of the Royal Statistical Society and Treasurer of the British Academy where he was a fellow. He is the author of "Basic Mathematics," "Mathematical Analysis for Economists," "Mathematical Economics" and "Macroeconomic Theory."

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting. It presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, environmental science, and finance. Along with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years, and contacts recent and relevant modeling developments and research challenges. New in the second edition: Expanded on aspects of core model theory and methodology. Multiple new examples and exercises. Detailed development of dynamic factor models. Updated discussion and connections with recent and current research frontiers.

Financial Economics and Econometrics provides an overview of the core topics in theoretical and empirical finance, with an emphasis on applications and interpreting results. Structured in five parts, the book covers financial data and univariate models; asset returns; interest rates, yields and spreads; volatility and correlation; and corporate finance and policy. Each chapter begins with a theory in financial economics, followed by econometric methodologies which have been used to explore the theory. Next, the chapter presents empirical evidence and discusses seminal papers on the topic. Boxes offer insights on how an idea can be applied to other disciplines such as management, marketing and medicine, showing the relevance of the material beyond finance. Readers are supported with plenty of worked examples and intuitive explanations throughout the book, while key takeaways, 'test your knowledge' and 'test your intuition' features at the end of each chapter also aid student learning. Digital supplements including PowerPoint slides, computer codes supplements, an Instructor's Manual and Solutions Manual are available for instructors. This textbook is suitable for upper-level undergraduate and graduate courses on financial economics, financial econometrics, empirical finance and related quantitative areas.

Extensive code examples in R, Stata, and Python Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions An easy-to-read conversational tone Up-to-date coverage of methods with fast-moving literatures like difference-in-differences

In Telos and Technos, Norman L. Roth breaks out of the strait-jacket of contemporary economic 'paradigms' with a clearly presented systematic remedy for our current economic theory that does not work in the real world of economic truths and consequences. For the first time, the static assumptions that have leeched so much of the credibility out of the dominant "neoclassical" models are put in their place. Truly dynamic concepts of technological time, change in consumer tastes and their measurable impact on the natural environment that must sustain us, are integrated into an interactive system of economic thought. This economic analysis and solution asks: "What are the causes of work?" How do they explain the official statistics of employment, unemployment, and labor participation? The assumption that full employment equilibrium is the natural state towards which an economy gravitates is jettisoned in favor of a far more realistic explanation of how a society really creates jobs. Serious limitations are revealed about our conceit that modern complex economics can be forced into "gyroscopic" stability by simply pressing the right buttons marked "interest rates" and "money-supply." Roth offers a vital and hopeful message to those who fear that modern economics has lost its way as a practical guide to modern society.

This book presents recent developments on the theoretical, algorithmic, and application aspects of Big Data in Complex and Social Networks. The book consists of four parts, covering a wide range of topics. The first part of the book focuses on data storage and data processing. It explores how the efficient storage of data can fundamentally support intensive data access and queries, which enables sophisticated analysis. It also looks at how data processing and visualization help to communicate information clearly and efficiently. The second part of the book is devoted to the extraction of essential information and the prediction of web content. The book shows how Big Data analysis can be used to understand the interests, location, and search history of users and provide more accurate predictions of User Behavior. The latter two parts of the book cover the protection of privacy and security, and emergent applications of big data and social networks. It analyzes how to model rumor diffusion, identify misinformation from massive data, and design intervention strategies. Applications of big data and social networks in multilayer networks and multiparty systems are also covered in-depth.

As conceived by the founders of the Econometric Society, econometrics is a field that uses economic theory and statistical methods to address empirical problems in economics. It is a tool for empirical discovery and policy analysis. The chapters in this volume embody this vision and either implement it directly or provide the tools for doing so. This vision is not shared by those who view econometrics as a branch of statistics rather than as a distinct field of knowledge that designs methods of inference from data based on models of human choice behavior and social interactions. All of the essays in this volume and its companion volume 6B offer guidance to the practitioner on how to apply the methods they discuss to interpret economic data. The authors of the chapters are all leading scholars in the fields they survey and extend.
*Part of the renowned Handbooks in Economics Series
*Updates and expands the exisiting "Handbook of Econometrics" volumes
*An invaluable reference written by some of the world's leading econometricians.

A fascinating and comprehensive history, this book explores the most important transformation in twentieth century economics: the creation of econometrics. Containing fresh archival material that has not been published before and taking Ragnar Frisch as the narrator, Francisco Louca discusses both the keys events - the establishment of the Econometric Society, the Cowles Commission and the journal Econometrica - and the major players - economists like Wesley Mitchell, mathematicians like John von Neumann and statisticians like Karl Pearson - in history that shaped the development of econometrics. He discusses the evolution of their thought, detailing the debates, the quarrels and the interrogations that crystallized their work and even offers a conclusion of sorts, suggesting that some of the more influential thinkers abandoned econometrics or became critical of its development. International in scope and appeal, The Years of High Econometrics is an excellent accompaniment for students taking courses on probability, econometric methods and the history of economic thought.

Examining the crucial topic of race relations, this book explores the economic and social environments that play a significant role in determining economic outcomes and why racial disparities persist.

With contributions from a range of international contributors including Edward Wolff and Catherine Weinberger, the book compares how various racial groups fare and are affected in different ways by economic and social institution. Themes covered in the book include:

• the economic status of various racial and ethnic groups, including their progress or retrenchment over the years
• how the law, economic motivations, and increased competition for jobs affect racial disparities.

This is an invaluable resource for researchers and academics across a number of disciplines including political economy, ethnic and multicultural studies, Asian studies, and sociology.

Bringing together leading-edge research and innovative energy markets econometrics, this book collects the author's most important recent contributions in energy economics. In particular, the book:* applies recent advances in the field of applied econometrics to investigate a number of issues regarding energy markets, including the theory of storage and the efficient markets hypothesis* presents the basic stylized facts on energy price movements using correlation analysis, causality tests, integration theory, cointegration theory, as well as recently developed procedures for testing for shared and codependent cycles* uses recent advances in the financial econometrics literature to model time-varying returns and volatility in energy prices and to test for causal relationships between energy prices and their volatilities* explores the functioning of electricity markets and applies conventional models of time series analysis to investigate a number of issues regarding wholesale power prices in the western North American markets* applies tools from statistics and dynamical systems theory to test for nonlinear dynamics and deterministic chaos in a number of North American hydrocarbon markets (those of ethane, propane, normal butane, iso-butane, naptha, crude oil, and natural gas)

"Applied Econometrics for Health Economists" introduces readers to the appropriate econometric techniques for use with different forms of survey data, known collectively as microeconometrics. The book provides a complete illustration of the steps involved in doing microeconometric research. The only study to deal with practical analysis of qualitative and categorical variables, it also emphasises applied work, illustrating the use of relevant computer software applied to large-scale survey datasets. This is a comprehensive reference guide - it contains a glossary of terms, a technical appendix, software appendix, references, and suggestions for further reading. It is concise and easy to read - technical details are avoided in the main text and key terms are highlighted. It is essential reading for health economists as well as undergraduate and postgraduate students of health economics. "Given the extensive use of individual-level survey data in health economics, it is important to understand the econometric techniques available to applied researchers. Moreover, it is just as important to be aware of their limitations and pitfalls. The purpose of this book is to introduce readers to the appropriate econometric techniques for use with different forms of survey data - known collectively as microeconometrics." - Andrew Jones, in the Preface.

The book first discusses in depth various aspects of the well-known inconsistency that arises when explanatory variables in a linear regression model are measured with error. Despite this inconsistency, the region where the true regression coeffecients lies can sometimes be characterized in a useful way, especially when bounds are known on the measurement error variance but also when such information is absent. Wage discrimination with imperfect productivity measurement is discussed as an important special case.

Next, it is shown that the inconsistency is not accidental but fundamental. Due to an identification problem, no consistent estimators may exist at all. Additional information is desirable. This information can be of various types. One type is exact prior knowledge about functions of the parameters. This leads to the CALS estimator. Another major type is in the form of instrumental variables. Many aspects of this are discussed, including heteroskedasticity, combination of data from different sources, construction of instruments from the available data, and the LIML estimator, which is especially relevant when the instruments are weak.

The scope is then widened to an embedding of the regression equation with measurement error in a multiple equations setting, leading to the exploratory factor analysis (EFA) model. This marks the step from measurement error to latent variables. Estimation of the EFA model leads to an eigenvalue problem. A variety of models is reviewed that involve eignevalue problems as their common characteristic.

EFA is extended to confirmatory factor analysis (CFA) by including restrictions on the parameters of the factor analysis model, and next by relating the factors to background variables.
These models are all structural equation models (SEMs), a very general and important class of models, with the LISREL model as its best-known representation, encompassing almost all linear equation systems with latent variables.

Estimation of SEMs can be viewed as an application of the generalized method of moments (GMM). GMM in general and for SEM in particular is discussed at great length, including the generality of GMM, optimal weighting, conditional moments, continuous updating, simulation estimation, the link with the method of maximum likelihood, and in particular testing and model evaluation for GMM.

The discussion concludes with nonlinear models. The emphasis is on polynomial models and models that are nonlinear due to a filter on the dependent variables, like discrete choice models or models with ordered categorical variables.

Written for graduate level students in advanced statistics, this handbook offers a comprehensive and practical overview of path analysis. A User's Guide to Path Analysis contains: * Definition and graphical illustrations of basic terms and concepts * Illustration of causal diagrams with emphasis on variable positioning, path symbols, error terms, missing arrows, and feedback loops * In-depth discussion of assumptions underlying path analysis * Discussion of causal model estimation with illustrations * Practical research questions for interpreting a path model * Instructions on how to read a path diagram, and how to use the SPSS computer program and interpret the results * Suggestions for what to include when writing up or interpreting findings

Written for graduate level students in advanced statistics, this handbook offers a comprehensive and practical overview of path analysis complete with: definition and graphical illustrations of basic terms and concepts; illustration of causal diagrams; in-depth discussion of assumptions underlying path analysis; discussion and illustration of causal model estimation; practical research questions for interpreting a path model; and instructions on how to read a path diagram and use the SPSS computer program.

Originally published in 1951, this volume reprints the classic work written by one of the leading global econometricians.
Econometrics is structured as followed:
Part 1 explains the relationship of Econometrics to Economics and Statistics.
Part 2 outlines the process of formulating economic hypotheses mathematically and of subjecting them to a statistical test.
Part 3 deals with the various component equations of the economic system - the psychic reactions, technical relations and reactions of business life and describes the process of setting up an economic model of the system as a whole.
Part 4 llustrates the use of econometric methods for policy purposes.

Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing.
*Addresses economic forecasting methodology, forecasting models, forecasting with different data structures, and the applications of forecasting methods
*Insights within this volume can be applied to economics, finance and marketing disciplines

Practically all donor countries that give aid claim to do so on the basis on the recipient's good governance, but do these claims have a real impact on the allocation of aid? Are democratic, human rights-respecting, countries with low levels of corruption and military expenditures actually likely to receive more aid than other countries?
Using econometric analysis, the author examines the factors that really determine the patterns of aid giving. The author analyses such examples as:
* aggregate aid flows
* aid from multilateral organisations such as the EU and the UN
* aid from bilateral donors such as Germany, Japan, the US as well as Arab donors.
This concise, well argued and well researched book will be a great read for students, academics and policy-makers involved in development studies, economics and international relations.

The explosive growth in computational power over the past several decades offers new tools and opportunities for economists. This handbook volume surveys recent research on Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting agents. Empirical referents for "agents" in ACE models can range from individuals or social groups with learning capabilities to physical world features with no cognitive function. Topics covered include: learning; empirical validation; network economics; social dynamics; financial markets; innovation and technological change; organizations; market design; automated markets and trading agents; political economy; social-ecological systems; computational laboratory development; and general methodological issues.
*Every volume contains contributions from leading researchers
*Each Handbook presents an accurate, self-contained survey of a particular topic
*The series provides comprehensive and accessible surveys

The "Theory of Macrojustice", introduced by S.-C. Kolm, is a stimulating contribution to the debate on the macroeconomic income distribution. The solution called "Equal Labour Income Equalisation" (ELIE) is the result of a three stages construction: collective agreement on the scheme of labour income redistribution, collective agreement on the degree of equalisation to be chosen in that framework, individual freedom to exploit his--her personal productive capicities (the source of labour income and the sole basis for taxation). This book is organised as a discussion around four complementary themes: philosophical aspects of macrojustice, economic analysis of macrojustice, combination of ELIE with other targeted tranfers, econometric evaluations of ELIE.