General Equilibrium Theory has been one of the major intellectual developments in economics during the past half-century. The theory of general equilibrium is centred on an inquiry about human societies which has several of the characteristics of a fundamental scientific question. In an economy, a multitude of agents produce, exchange, and consume a large number of commodities. Their decisions are independent of each other and dictated by self-interest. Attempting to answer the question 'Why is social chaos not the result?' has required an intensive research effort by several generations of leading economists.This important three volume set gathers together many of the articles that have played an influential role in the history of ideas in the general equilibrium area in the contemporary period.

Economic Models for Industrial Organization focuses on the specification and estimation of econometric models for research in industrial organization. In recent decades, empirical work in industrial organization has moved towards dynamic and equilibrium models, involving econometric methods which have features distinct from those used in other areas of applied economics. These lecture notes, aimed for a first or second-year PhD course, motivate and explain these econometric methods, starting from simple models and building to models with the complexity observed in typical research papers. The covered topics include discrete-choice demand analysis, models of dynamic behavior and dynamic games, multiple equilibria in entry games and partial identification, and auction models.

Introduction to Statistics with SPSS offers an introduction to statistics that can be used before, during or after a course on statistics. Covering a wide range of terms and techniques, including simple and multiple regressions, this book guides the student to enter data from a simple research project into a computer, provide an adequate analysis of the data and present a report on the findings.

The "Contributions to Economic Analysis" series consists of a number of previously unpublished studies. The term economic analysis is used because it covers the activities of the theoretical economist and the research worker.

This short book introduces the main ideas of statistical inference in a way that is both user friendly and mathematically sound. Particular emphasis is placed on the common foundation of many models used in practice. In addition, the book focuses on the formulation of appropriate statistical models to study problems in business, economics, and the social sciences, as well as on how to interpret the results from statistical analyses. The book will be useful to students who are interested in rigorous applications of statistics to problems in business, economics and the social sciences, as well as students who have studied statistics in the past, but need a more solid grounding in statistical techniques to further their careers. Jacco Thijssen is professor of finance at the University of York, UK. He holds a PhD in mathematical economics from Tilburg University, Netherlands. His main research interests are in applications of optimal stopping theory, stochastic calculus, and game theory to problems in economics and finance. Professor Thijssen has earned several awards for his statistics teaching.

The purpose of this book is to introduce novice researchers to the tools of meta-analysis and meta-regression analysis and to summarize the state of the art for existing practitioners. Meta-regression analysis addresses the rising "Tower of Babel" that current economics and business research has become. Meta-analysis is the statistical analysis of previously published, or reported, research findings on a given hypothesis, empirical effect, phenomenon, or policy intervention. It is a systematic review of all the relevant scientific knowledge on a specific subject and is an essential part of the evidence-based practice movement in medicine, education and the social sciences. However, research in economics and business is often fundamentally different from what is found in the sciences and thereby requires different methods for its synthesis-meta-regression analysis. This book develops, summarizes, and applies these meta-analytic methods.

This book brings together cutting edge contributions in the fields of international economics, micro theory, welfare economics and econometrics, with contributions from Donald R. Davis, Avinash K. Dixit, Tadashi Inoue, Ronald W. Jones, Dale W. Jorgenson, K. Rao Kadiyala, Murray C. Kemp, Kenneth M. Kletzer, Anne O. Krueger, Mukul Majumdar, Daniel McFadden, Lionel McKenzie, James R. Melvin, James C. Moore, Takashi Negishi, Yoshihiko Otani, Raymond Riezman, Paul A. Samuelson, Joaquim Silvestre and Marie Thursby.

This book presents some of Arnold Zellner's outstanding contributions to the philosophy, theory and application of Bayesian analysis, particularly as it relates to statistics, econometrics and economics. The volume contains both previously published and new material which cite and discuss the work of Bayesians who have made a contribution by helping researchers and analysts in many professions to become more effective in learning from data and making decisions. Bayesian and non-Bayesian approaches are compared in several papers. Other articles include theoretical and applied results on estimation, model comparison, prediction, forecasting, prior densities, model formulation and hypothesis testing. In addition, a new information processing approach is presented that yields Bayes's Theorem as a perfectly efficient information processing rule. This volume will be essential reading for academics and students interested in qualitative methods as well as industrial analysts and government officials.

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box-Muller and Metropolis-Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science.

This text focuses on two key components of microeconomics - optimization subject to constraints and the development of comparative statics. It assumes familiarity with calculus of one variable and basic linear algebra, allowing more extensive coverage of additional topics like constrained optimization, the chain rule, Taylor's theorem, line integrals and dynamic programming. The book contains numerous examples that illustrate economics and mathematical situations, many with complete solutions.;"Mathematics for Economists" provides a collection of topics to complement first semester PhD microeconomics course. It contains the mathematical material necessary as background for topics covered in graduate level microeconomics courses.

* Starts from the basics, focusing less on proofs and the high-level math underlying regressions, and adopts an engaging tone to provide a text which is entirely accessible to students who don't have a stats background * New chapter on integrity and ethics in regression analysis * Each chapter offers boxed examples, stories, exercises and clear summaries, all of which are designed to support student learning * Optional appendix of statistical tools, providing a primer to readers who need it * Code in R and Stata, and data sets and exercises in Stata and CSV, to allow students to practice running their own regressions * Author-created videos on YouTube * PPT lecture slides and test bank for instructors

This thoroughly revised second edition of an upper-level undergraduate/graduate text describes many major techniques of forecasting used in economics and business. This is the only time series book to concentrate on the forecasting of economic data and to cover such a broad range of topics. The key features are: explains how to specify and evaluate simple models from the time series and econometric approaches; places special emphasis on the information that is derived from the evaluation and combinations of forecasts; discusses the topics of technological and population forecasting; includes an expanded chapter on regression techniques; presents a practical forecasting project which runs throughout the text; includes an appendix on basic statistical concepts.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

Dedicated to the measurement and definition of market power across a broad array of industries, this text should provide economists and lawyers with an insight into how anti-trust economists actually go about measuring market power in a given industry. The "Contributions to Economic Analysis" series consists of a number of previously unpublished studies. The term economic analysis is used because it covers the activities of the theoretical economist and the research worker.

In the 16th Edition of "Advances in Econometrics", we present twelve papers discussing the current interface between Marketing and Econometrics. The authors are leading scholars in the fields and introduce the latest models for analysing marketing data. The papers are representative of the types of problems and methods that are used within the field of marketing. Marketing focuses on the interaction between the firm and the consumer. Economics encompasses this interaction as well as many others. Economics, along with psychology and sociology, provides a theoretical foundation for marketing. Given the applied nature of marketing research, measurement and quantitative issues arise frequently. Quantitative marketing tends to rely heavily upon statistics and econometrics. However, quantitative marketing can place a different emphasis upon the problem than econometrics, even when using the same techniques. A basic difference between quantitative marketing research and econometrics tends to be the pragmatism that is found in many marketing studies. Another important motivating factor in marketing research is the type of data that is available. Applied econometrics tends to rely heavily on data collected by governmental organizations. In contrast, marketing often uses data collected by private firms or marketing research firms. Observational and survey data are quite similar to those used in econometrics. However, the remaining types of data, panel and transactional, can look quite different from what may be familiar to econometricians. The automation and computerization of much of the sales transaction process leaves an audit trail that results in huge quantities of data. A popular area of study is the use of scanner data collected at the checkout stand using bar code readers. Methods that work for small data sets may not work well in these larger data sets. In addition, new sources of data, such as clickstream data from a web site, will offer new challenges. This volume addresses these and related issues.

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors' E4 MATLAB (R) toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.

Heavy tails -extreme events or values more common than expected -emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks the natural emergence of heavy-tailed distributions from a wide variety of general processes, building intuition. And it reveals the controversy surrounding heavy tails to be the result of flawed statistics, then equips readers to identify and estimate with confidence. Over 100 exercises complete this engaging package.

Ranking of Multivariate Populations: A Permutation Approach with Applications presents a novel permutation-based nonparametric approach for ranking several multivariate populations. Using data collected from both experimental and observation studies, it covers some of the most useful designs widely applied in research and industry investigations, such as multivariate analysis of variance (MANOVA) and multivariate randomized complete block (MRCB) designs. The first section of the book introduces the topic of ranking multivariate populations by presenting the main theoretical ideas and an in-depth literature review. The second section discusses a large number of real case studies from four specific research areas: new product development in industry, perceived quality of the indoor environment, customer satisfaction, and cytological and histological analysis by image processing. A web-based nonparametric combination global ranking software is also described. Designed for practitioners and postgraduate students in statistics and the applied sciences, this application-oriented book offers a practical guide to the reliable global ranking of multivariate items, such as products, processes, and services, in terms of the performance of all investigated products/prototypes.

Now in its third edition, Essential Econometric Techniques: A Guide to Concepts and Applications is a concise, student-friendly textbook which provides an introductory grounding in econometrics, with an emphasis on the proper application and interpretation of results. Drawing on the author's extensive teaching experience, this book offers intuitive explanations of concepts such as heteroskedasticity and serial correlation, and provides step-by-step overviews of each key topic. This new edition contains more applications, brings in new material including a dedicated chapter on panel data techniques, and moves the theoretical proofs to appendices. After Chapter 7, students will be able to design and conduct rudimentary econometric research. The next chapters cover multicollinearity, heteroskedasticity, and autocorrelation, followed by techniques for time-series analysis and panel data. Excel data sets for the end-of-chapter problems are available as a digital supplement. A solutions manual is also available for instructors, as well as PowerPoint slides for each chapter. Essential Econometric Techniques shows students how economic hypotheses can be questioned and tested using real-world data, and is the ideal supplementary text for all introductory econometrics courses.

This book provides a comprehensive overview of the fruitful achievement of China's Quantitative Economics during the past 30 years, assembling pioneering contributions of prominent quantitative economists in China. It chronicles significant events and the detailed evolution of Quantitative Economics in China. This well-organized book is a must-have for scholars to get a full picture of the status quo, and identify possible research gaps.

This new book will be welcomed by econometricians and students of econometrics everywhere. Introducing discrete time modelling techniques and bridging the gap between economics and econometric literature, this ambitious book is sure to be an invaluable resource for all those to whom the terms unit roots, cointegration and error correction forms, chaos theory and random walks are recognisable if not yet fully understood.

Environmental risk directly affects the financial stability of banks since they bear the financial consequences of the loss of liquidity of the entities to which they lend and of the financial penalties imposed resulting from the failure to comply with regulations and for actions taken that are harmful to the natural environment. This book explores the impact of environmental risk on the banking sector and analyzes strategies to mitigate this risk with a special emphasis on the role of modelling. It argues that environmental risk modelling allows banks to estimate the patterns and consequences of environmental risk on their operations, and to take measures within the context of asset and liability management to minimize the likelihood of losses. An important role here is played by the environmental risk modelling methodology as well as the software and mathematical and econometric models used. It examines banks' responses to macroprudential risk, particularly from the point of view of their adaptation strategies; the mechanisms of its spread; risk management and modelling; and sustainable business models. It introduces the basic concepts, definitions, and regulations concerning this type of risk, within the context of its influence on the banking industry. The book is primarily based on a quantitative and qualitative approach and proposes the delivery of a new methodology of environmental risk management and modelling in the banking sector. As such, it will appeal to researchers, scholars, and students of environmental economics, finance and banking, sociology, law, and political sciences.

This book provides an economic and econometric analysis of real estate investment and real estate market behaviour. Peijie Wang examines fluctuations in the real estate business to reveal the mechanisms governing the interactions between the industry and other sectors of the economy.

The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.