Companion Website materials: https://tzkeith.com/ Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: * Covers both MR and SEM, while explaining their relevance to one another * Includes path analysis, confirmatory factor analysis, and latent growth modeling * Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises * Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: * New chapter on mediation, moderation, and common cause * New chapter on the analysis of interactions with latent variables and multilevel SEM * Expanded coverage of advanced SEM techniques in chapters 18 through 22 * International case studies and examples * Updated instructor and student online resources

World Statistics on Mining and Utilities 2018 provides a unique biennial overview of the role of mining and utility activities in the world economy. This extensive resource from UNIDO provides detailed time series data on the level, structure and growth of international mining and utility activities by country and sector. Country level data is clearly presented on the number of establishments, employment and output of activities such as: coal, iron ore and crude petroleum mining as well as production and supply of electricity, natural gas and water. This unique and comprehensive source of information meets the growing demand of data users who require detailed and reliable statistical information on the primary industry and energy producing sectors. The publication provides internationally comparable data to economic researchers, development strategists and business communities who influence the policy of industrial development and its environmental sustainability.

Covering a broad range of topics, this text provides a comprehensive survey of the modeling of chaotic dynamics and complexity in the natural and social sciences. Its attention to models in both the physical and social sciences and the detailed philosophical approach make this a unique text in the midst of many current books on chaos and complexity. Including an extensive index and bibliography along with numerous examples and simplified models, this is an ideal course text.

Change of Time and Change of Measure provides a comprehensive account of two topics that are of particular significance in both theoretical and applied stochastics: random change of time and change of probability law.Random change of time is key to understanding the nature of various stochastic processes, and gives rise to interesting mathematical results and insights of importance for the modeling and interpretation of empirically observed dynamic processes. Change of probability law is a technique for solving central questions in mathematical finance, and also has a considerable role in insurance mathematics, large deviation theory, and other fields.The book comprehensively collects and integrates results from a number of scattered sources in the literature and discusses the importance of the results relative to the existing literature, particularly with regard to mathematical finance.In this Second Edition a Chapter 13 entitled 'A Wider View' has been added. This outlines some of the developments that have taken place in the area of Change of Time and Change of Measure since the publication of the First Edition. Most of these developments have their root in the study of the Statistical Theory of Turbulence rather than in Financial Mathematics and Econometrics, and they form part of the new research area termed 'Ambit Stochastics'.

This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction, volatility and the impact of different orders of integration. Models with expectations are considered along with alternate methods such as Singular Spectrum Analysis (SSA), the Kalman Filter and Structural Time Series, all in relation to cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide direction and guidance to the now vast literature facing students and graduate economists.

Advanced Statistics for Kinesiology and Exercise Science is the first textbook to cover advanced statistical methods in the context of the study of human performance. Divided into three distinct sections, the book introduces and explores in depth both analysis of variance (ANOVA) and regressions analyses, including chapters on: preparing data for analysis; one-way, factorial, and repeated-measures ANOVA; analysis of covariance and multiple analyses of variance and covariance; diagnostic tests; regression models for quantitative and qualitative data; model selection and validation; logistic regression Drawing clear lines between the use of IBM SPSS Statistics software and interpreting and analyzing results, and illustrated with sport and exercise science-specific sample data and results sections throughout, the book offers an unparalleled level of detail in explaining advanced statistical techniques to kinesiology students. Advanced Statistics for Kinesiology and Exercise Science is an essential text for any student studying advanced statistics or research methods as part of an undergraduate or postgraduate degree programme in kinesiology, sport and exercise science, or health science.

Originally published in 1984. This book examines two important dimensions of efficiency in the foreign exchange market using econometric techniques. It responds to the macroeconomics trend to re-examining the theories of exchange rate determination following the erratic behaviour of exchange rates in the late 1970s. In particular the text looks at the relation between spot and forward exchange rates and the term structure of the forward premium, both of which require a joint test of market efficiency and the equilibrium model. Approaches used are the regression of spot rates on lagged forward rates and an explicit time series analysis of the spot and forward rates, using data from Canada, the United Kingdom, the Netherlands, Switzerland and Germany.

Originally published in 1979. This book addresses three questions regarding uncertainty in economic life: how do we define uncertainty and use the concept meaningfully to provide conclusions; how can the level of uncertainty associated with a particular variable of economic interest be measured; and does experience provide any support for the view that uncertainty really matters. It develops a theory of the effect of price uncertainty on production and trade, takes a graphical approach to look at effects of a mean preserving spread to create rules for ordering distributions, and finishes with an econometric analysis of the effects of Brazil's adoption of a crawling peg in reducing real exchange rate uncertainty. This is an important early study into the significance of uncertainty.

Econometric Model Specification reviews and extends the author's papers on consistent model specification testing and semi-nonparametric modeling and inference. This book consists of two parts. The first part discusses consistent tests of functional form of regression and conditional distribution models, including a consistent test of the martingale difference hypothesis for time series regression errors. In the second part, semi-nonparametric modeling and inference for duration and auction models are considered, as well as a general theory of the consistency and asymptotic normality of semi-nonparametric sieve maximum likelihood estimators. Moreover, this volume also contains addendums and appendices that provide detailed proofs and extensions of all the results. It is uniquely self-contained and is a useful source for students and researchers interested in model specification issues.

In these two volumes, a group of distinguished economists debate the way in which evidence, in particular econometric evidence, can and should be used to relate macroeconomic theories to the real world. Topics covered include the business cycle, monetary policy, economic growth, the impact of new econometric techniques, the IS-LM model, the labour market, new Keynesian macroeconomics, and the use of macroeconomics in official documents.

Since the middle of the twentieth century, economists have invested great resources into using statistical evidence to relate macroeconomic theories to the real world, and many new econometric techniques have been employed. In these two volumes, a distinguished group of economic theorists, econometricians, and economic methodologists examine how evidence has been used and how it should be used to understand the real world. Volume 1 focuses on the contribution of econometric techniques to understanding the macroeconomic world. It covers the use of evidence to understand the business cycle, the operation of monetary policy, and economic growth. A further section offers assessments of the overall impact of recent econometric techniques such as cointegration and unit roots. Volume 2 focuses on the labour market and economic policy, with sections covering the IS-LM model, the labour market, new Keynesian macroeconomics, and the use of macroeconomics in official documents (in both the USA and the EU). These volumes will be valuable to advanced undergraduates, graduate students, and practitioners for their clear presentation of opposing perspectives on macroeconomics and how evidence should be used. The chapters are complemented by discussion sections revealing the perspectives of other contributors on the methodological issues raised.

Prepares readers to analyze data and interpret statistical results using the increasingly popular R more quickly than other texts through LessR extensions which remove the need to program. By introducing R through less R, readers learn how to organize data for analysis, read the data into R, and produce output without performing numerous functions and programming first. Readers can select the necessary procedure and change the relevant variables without programming. Quick Starts introduce readers to the concepts and commands reviewed in the chapters. Margin notes define, illustrate, and cross-reference the key concepts. When readers encounter a term previously discussed, the margin notes identify the page number to the initial introduction. Scenarios highlight the use of a specific analysis followed by the corresponding R/lessR input and an interpretation of the resulting output. Numerous examples of output from psychology, business, education, and other social sciences demonstrate how to interpret results and worked problems help readers test their understanding. www.lessRstats.com website features the lessR program, the book's 2 data sets referenced in standard text and SPSS formats so readers can practice using R/lessR by working through the text examples and worked problems, PDF slides for each chapter, solutions to the book's worked problems, links to R/lessR videos to help readers better understand the program, and more. New to this edition: o upgraded functionality and data visualizations of the lessR package, which is now aesthetically equal to the ggplot 2 R standard o new features to replace and extend previous content, such as aggregating data with pivot tables with a simple lessR function call.

Originally published in 1984. Since the logic underlying economic theory can only be grasped fully by a thorough understanding of the mathematics, this book will be invaluable to economists wishing to understand vast areas of important research. It provides a basic introduction to the fundamental mathematical ideas of topology and calculus, and uses these to present modern singularity theory and recent results on the generic existence of isolated price equilibria in exchange economies.

"Mathematical Optimization and Economic Analysis" is a self-contained introduction to various optimization techniques used in economic modeling and analysis such as geometric, linear, and convex programming and data envelopment analysis. Through a systematic approach, this book demonstrates the usefulness of these mathematical tools in quantitative and qualitative economic analysis.

The book presents specific examples to demonstrate each technique's advantages and applicability as well as numerous applications of these techniques to industrial economics, regulatory economics, trade policy, economic sustainability, production planning, and environmental policy.

Key Features include:

- A detailed presentation of both single-objective and multiobjective optimization;

- An in-depth exposition of various applied optimization problems;

- Implementation of optimization tools to improve the accuracy of various economic models;

- Extensive resources suggested for further reading.

This book is intended for graduate and postgraduate students studying quantitative economics, as well as economics researchers and applied mathematicians. Requirements include a basic knowledge of calculus and linear algebra, and a familiarity with economic modeling.

This book is based on two Sir Richard Stone lectures at the Bank of England and the National Institute for Economic and Social Research. Largely non-technical, the first part of the book covers some of the broader issues involved in Stone's and others' work in statistics. It explores the more philosophical issues attached to statistics, econometrics and forecasting and describes the paradigm shift back to the Bayesian approach to scientific inference. The first part concludes with simple examples from the different worlds of educational management and golf clubs. The second, more technical part covers in detail the structural econometric time series analysis (SEMTSA) approach to statistical and econometric modeling.

Introduction to Financial Mathematics: Option Valuation, Second Edition is a well-rounded primer to the mathematics and models used in the valuation of financial derivatives. The book consists of fifteen chapters, the first ten of which develop option valuation techniques in discrete time, the last five describing the theory in continuous time. The first half of the textbook develops basic finance and probability. The author then treats the binomial model as the primary example of discrete-time option valuation. The final part of the textbook examines the Black-Scholes model. The book is written to provide a straightforward account of the principles of option pricing and examines these principles in detail using standard discrete and stochastic calculus models. Additionally, the second edition has new exercises and examples, and includes many tables and graphs generated by over 30 MS Excel VBA modules available on the author's webpage https://home.gwu.edu/~hdj/.

In order to make informed decisions, there are three important elements: intuition, trust, and analytics. Intuition is based on experiential learning and recent research has shown that those who rely on their "gut feelings" may do better than those who don't. Analytics, however, are important in a data-driven environment to also inform decision making. The third element, trust, is critical for knowledge sharing to take place. These three elements-intuition, analytics, and trust-make a perfect combination for decision making. This book gathers leading researchers who explore the role of these three elements in the process of decision-making.

Offering a unique balance between applications and calculations, Monte Carlo Methods and Models in Finance and Insurance incorporates the application background of finance and insurance with the theory and applications of Monte Carlo methods. It presents recent methods and algorithms, including the multilevel Monte Carlo method, the statistical Romberg method, and the Heath-Platen estimator, as well as recent financial and actuarial models, such as the Cheyette and dynamic mortality models. The authors separately discuss Monte Carlo techniques, stochastic process basics, and the theoretical background and intuition behind financial and actuarial mathematics, before bringing the topics together to apply the Monte Carlo methods to areas of finance and insurance. This allows for the easy identification of standard Monte Carlo tools and for a detailed focus on the main principles of financial and insurance mathematics. The book describes high-level Monte Carlo methods for standard simulation and the simulation of stochastic processes with continuous and discontinuous paths. It also covers a wide selection of popular models in finance and insurance, from Black-Scholes to stochastic volatility to interest rate to dynamic mortality. Through its many numerical and graphical illustrations and simple, insightful examples, this book provides a deep understanding of the scope of Monte Carlo methods and their use in various financial situations. The intuitive presentation encourages readers to implement and further develop the simulation methods.

Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observations. It explores a wide range of alternative topics, including maximum likelihood and Bayesian estimation, various types of spatial regression specifications, and applied modeling situations involving different circumstances. Leaders in this field, the authors clarify the often-mystifying phenomenon of simultaneous spatial dependence. By presenting new methods, they help with the interpretation of spatial regression models, especially ones that include spatial lags of the dependent variable. The authors also examine the relationship between spatiotemporal processes and long-run equilibrium states that are characterized by simultaneous spatial dependence. MATLAB (R) toolboxes useful for spatial econometric estimation are available on the authors' websites. This work covers spatial econometric modeling as well as numerous applied illustrations of the methods. It encompasses many recent advances in spatial econometric models-including some previously unpublished results.

There is no shortage of incentives to study and reduce poverty in our societies. Poverty is studied in economics and political sciences, and population surveys are an important source of information about it. The design and analysis of such surveys is principally a statistical subject matter and the computer is essential for their data compilation and processing. Focusing on The European Union Statistics on Income and Living Conditions (EU-SILC), a program of annual national surveys which collect data related to poverty and social exclusion, Statistical Studies of Income, Poverty and Inequality in Europe: Computing and Graphics in R presents a set of statistical analyses pertinent to the general goals of EU-SILC. The contents of the volume are biased toward computing and statistics, with reduced attention to economics, political and other social sciences. The emphasis is on methods and procedures as opposed to results, because the data from annual surveys made available since publication and in the near future will degrade the novelty of the data used and the results derived in this volume. The aim of this volume is not to propose specific methods of analysis, but to open up the analytical agenda and address the aspects of the key definitions in the subject of poverty assessment that entail nontrivial elements of arbitrariness. The presented methods do not exhaust the range of analyses suitable for EU-SILC, but will stimulate the search for new methods and adaptation of established methods that cater to the identified purposes.

In this testament to the distinguished career of H.S. Houthakker a number of Professor Houthakker's friends, former colleagues and former students offer essays which build upon and extend his many contributions to economics in aggregation, consumption, growth and trade. Among the many distinguished contributors are Paul Samuelson, Werner Hildenbrand, John Muellbauer and Lester Telser. The book also includes four previously unpublished papers and notes by its distinguished dedicatee.

An insightful and up-to-date study of the use of periodic models in the description and forecasting of economic data. Incorporating recent developments in the field, the authors investigate such areas as seasonal time series; periodic time series models; periodic integration; and periodic cointegration. The analysis from the inclusion of many new empirical examples and results. Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

Originally published in 1976 and with second edition published in 1984. This book established itself as the first genuinely introductory text on econometric methods, assuming no formal background on the part of the reader. The second edition maintains this distinctive feature. Fundamental concepts are carefully explained and, where possible, techniques are developed by verbal reasoning rather than formal proof. It provides all the material for a basic course. and is also ideal for a student working alone. Very little knowledge of maths and statistics is assumed, and the logic of statistical method is carefully stated. There are numerous exercises, designed to help the student assess individual progress. Methods are described with computer solutions in mind and the author shows how a variety of different calculations can be performed with relatively simple programs. This new edition also includes much new material - statistical tables are now included and their use carefully explained.

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 1985. Mathematical methods and models to facilitate the understanding of the processes of economic dynamics and prediction were refined considerably over the period before this book was written. The field had grown; and many of the techniques involved became extremely complicated. Areas of particular interest include optimal control, non-linear models, game-theoretic approaches, demand analysis and time-series forecasting. This book presents a critical appraisal of developments and identifies potentially productive new directions for research. It synthesises work from mathematics, statistics and economics and includes a thorough analysis of the relationship between system understanding and predictability.