Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems. The material of the book can be roughly divided into four parts of approximately equal length: 1) basic concepts and techniques of FDA, 2) functional regression models, 3) sparse and dependent functional data, and 4) introduction to the Hilbert space framework of FDA. The book assumes advanced undergraduate background in calculus, linear algebra, distributional probability theory, foundations of statistical inference, and some familiarity with R programming. Other required statistics background is provided in scalar settings before the related functional concepts are developed. Most chapters end with references to more advanced research for those who wish to gain a more in-depth understanding of a specific topic.

" …deals rigorously with many of the problems that have bedevilled the subject up to the present time…" — Stephen Pollock, Econometric Theory

"I continued to be pleasantly surprised by the variety and usefulness of its contents " — Isabella Verdinelli, Journal of the American Statistical Association

Continuing the success of their first edition, Magnus and Neudecker present an exhaustive and self-contained revised text on matrix theory and matrix differential calculus. Matrix calculus has become an essential tool for quantitative methods in a large number of applications, ranging from social and behavioural sciences to econometrics. While the structure and successful elements of the first edition remain, this revised and updated edition contains many new examples and exercises.

• Contains the essentials of multivariable calculus with an emphasis on the use of differentials
• Many new examples and exercises
• Fulfils the need for a unified and self-contained treatment of matrix differential calculus
• Includes new developments in this field

In order to understand and formulate housing policy and programs, it is necessary to have a working knowledge of the internal economic operation of housing from the points of view of both the investor and the owner. James W. Hughes argues that investors' and owners' behavior and activity tend to be governed by market forces and other realities. In that regard, he begins this work by analyzing market rates of return in real estate and housing undertakings, and the variety of analytical techniques which underlie their determination.

Methods of Housing Analysis is designed to provide urban planners with an introduction to the basic, quantitative techniques associated with the analysis of housing. A myriad of specific analytical methods has evolved in each of the professions concerned with this subject area. Planners, investors, developers, engineers, appraisers, social scientists, and governmental officials all tend to exhibit unique perspectives when examining housing and have developed their analytical frameworks accordingly.

The work is comprised of an extensive discussion by the author, detailed case studies and examples, and a number of essays by leading experts that detail specific analytical procedures and demonstrate their use. The book is divided into four major sections: analysis of the internal operation of housing; basic cost-revenue analysis; expanded cost-revenue/benefit analysis; and government regulation of housing. The thorough nature of Hughes' discussion and of the related readings makes this volume an ideal textbook and reference source.

Developed from the author's course on Monte Carlo simulation at Brown University, Monte Carlo Simulation with Applications to Finance provides a self-contained introduction to Monte Carlo methods in financial engineering. It is suitable for advanced undergraduate and graduate students taking a one-semester course or for practitioners in the financial industry.

The author first presents the necessary mathematical tools for simulation, arbitrary free option pricing, and the basic implementation of Monte Carlo schemes. He then describes variance reduction techniques, including control variates, stratification, conditioning, importance sampling, and cross-entropy. The text concludes with stochastic calculus and the simulation of diffusion processes.

Only requiring some familiarity with probability and statistics, the book keeps much of the mathematics at an informal level and avoids technical measure-theoretic jargon to provide a practical understanding of the basics. It includes a large number of examples as well as MATLAB(r) coding exercises that are designed in a progressive manner so that no prior experience with MATLAB is needed.

Originally published in 1981, this book considers one particular area of econometrics- the linear model- where significant recent advances have been made. It considers both single and multiequation models with varying co-efficients, explains the various theories and techniques connected with these and goes on to describe the various applications of the models. Whilst the detailed explanation of the models will interest primarily econometrics specialists, the implications of the advances outlined and the applications of the models will intrest a wide range of economists.

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.

This work is an examination of borderless markets where national boundaries are no longer the relevant criteria in making international marketing, economic planning, and business decisions. Understanding nonpolitical borders is especially important for products and industries that are culture bound and those that require local adaptation. Language is often one critical factor that affects economic development, demographic behavior, and general business policies around the world. Over 130,000 statistics are provided for over 460 language groups covering a number of social, economic, and business variables. A significant review of literature is also included.

Volume 27 of "Advances in Econometrics", entitled "Missing Data Methods", contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; Consistent Estimation and Orthogonality; and Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling.

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website

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.

Leverage the full power of Bayesian analysis for competitive advantage Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and move toward R to do even more. Step by step, with real-world examples, Carlberg shows you how to use Bayesian analytics to solve a wide array of real problems. Carlberg clarifies terminology that often bewilders analysts, and offers sample R code to take advantage of the rethinking package in R and its gateway to Stan. As you incorporate these Bayesian approaches into your analytical toolbox, you'll build a powerful competitive advantage for your organization-and yourself. Explore key ideas and strategies that underlie Bayesian analysis Distinguish prior, likelihood, and posterior distributions, and compare algorithms for driving sampling inputs Use grid approximation to solve simple univariate problems, and understand its limits as parameters increase Perform complex simulations and regressions with quadratic approximation and Richard McElreath's quap function Manage text values as if they were numeric Learn today's gold-standard Bayesian sampling technique: Markov Chain Monte Carlo (MCMC) Use MCMC to optimize execution speed in high-complexity problems Discover when frequentist methods fail and Bayesian methods are essential-and when to use both in tandem

The small sample properties of estimators and tests are frequently too complex to be useful or are unknown. Much econometric theory is therefore developed for very large or asymptotic samples where it is assumed that the behaviour of estimators and tests will adequately represent their properties in small samples. Refined asymptotic methods adopt an intermediate position by providing improved approximations to small sample behaviour using asymptotic expansions. Dedicated to the memory of Michael Magdalinos, whose work is a major contribution to this area, this book contains chapters directly concerned with refined asymptotic methods. In addition, there are chapters focussing on new asymptotic results; the exploration through simulation of the small sample behaviour of estimators and tests in panel data models; and improvements in methodology. With contributions from leading econometricians, this collection will be essential reading for researchers and graduate students concerned with the use of asymptotic methods in econometric analysis.

As Ken Wallis (1993) has pOinted out, all macroeconomic forecasters and policy analysts use economic models. That is, they have a way of going from assumptions about macroeconomic policy and the international environment, to a prediction of the likely future state of the economy. Some people do this in their heads. Increasingly though, forecasting and policy analysis is based on a formal, explicit model, represented by a set of mathematical equations and solved by computer. This provides a framework for handling, in a consistent and systematic manner, the ever-increasing amounts of relevant information. Macroeconometric modelling though, is an inexact science. A manageable model must focus only on the major driving forces in a complex economy made up of millions of households and fIrms. International economic agencies such as the IMF and OECD, and most treasuries and central banks in western countries, use macroeconometric models in their forecasting and policy analysis. Models are also used for teaching and research in universities, as well as for commercial forecasting in the private sector.

This book surveys existing similar econometric models in Japan and offers several econometric models combining Japan, the US and other Asia-Pacific countries. These models have been explored by the author and his group at Nagoya University and other institutions for three decades, and are applied for the following four objectives. First, they construct a world econometric model of industry and trade, and thereby quantitatively assess the impacts of protective US trade policies and Japan's technical progress on Asia-Pacific economies. Second, they use an international input-output table, including China, to analyze the interdependence between Japanese firms with the subsidiaries in the US and Asia, and other foreign companies. Third, they use a small link model of China, Japan, Korea and the US, and thereby evaluate the macroeconomic effects of the respective fiscal policies. Fourth, they offer a multi-sector econometric model of the interactions pertaining to economic activity, energy and environment in China, and assess the effects of improved energy efficiency and demand shift in China.This volume comprises papers written by Soshichi Kinoshita (Professor Emeritus, Nagoya University, Nagoya), Jiro Nemoto (Professor of Economics, Nagoya University, Nagoya), Mitsuo Yamada (Professor of Economics, Chukyo University, Nagoya) and Taiyo Ozaki (Professor of Economics, Kyoto Gakuen University, Kyoto).

In recent years there has been a substantial global increase in interest in the study of gambling. To some extent this has mirrored seismic changes in the way that betting and gaming markets worldwide are taxed and regulated. This has heightened interest in a wide range of issues related to this sector including its regulation, public policy and commercial strategy as well as the ideal structure of gambling taxes and devising optimal responses to environmental changes, such as the growth of online gambling. This volume, by bringing together the work of leading scholars, will cover the spectrum of such perspectives, as well as examining the efficiency of betting markets, to provide an assessment of developments and current understanding in the study of the economics of gambling. This timely collection will be an immensely valuable resource for academics, policy-makers, those commercially involved in the betting and gaming sectors as well as the interested layman.

Volumes 45a and 45b of Advances in Econometrics honor Joon Y. Park, Wisnewsky Professor of Human Studies and Professor of Economics at Indiana University. Professor Park has made numerous and substantive contributions to the field of econometrics since beginning his academic career in the mid-1980s and has held positions at Cornell University, University of Toronto, Seoul National University, Rice University, Texas A&M University, and Sungkyunkwan University. This first volume, Essays in Honor of Joon Y. Park: Econometric Theory, features contributions to econometric theory related to Professor Park’s analysis of time series and particularly related to the research of the first two or so decades of his career.

Volume 27 of "Advances in Econometrics", entitled "Missing Data Methods", contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; Consistent Estimation and Orthogonality; and Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling.

This book brings together domains in financial asset pricing and valuation, financial investment theory, econometrics modeling, and the empirical analyses of financial data by applying appropriate econometric techniques. These domains are highly intertwined and should be properly understood in order to correctly and effectively harness the power of data and methods for investment and financial decision-making. The book is targeted at advanced finance undergraduates and beginner professionals performing financial forecasts or empirical modeling who will find it refreshing to see how forecasting is not simply running a least squares regression line across data points, and that there are many minefields and pitfalls to avoid, such as spurious results and incorrect interpretations.

Showcasing fuzzy set theory, this book highlights the enormous potential of fuzzy logic in helping to analyse the complexity of a wide range of socio-economic patterns and behaviour. The contributions to this volume explore the most up-to-date fuzzy-set methods for the measurement of socio-economic phenomena in a multidimensional and/or dynamic perspective. Thus far, fuzzy-set theory has primarily been utilised in the social sciences in the field of poverty measurement. These chapters examine the latest work in this area, while also exploring further applications including social exclusion, the labour market, educational mismatch, sustainability, quality of life and violence against women. The authors demonstrate that real-world situations are often characterised by imprecision, uncertainty and vagueness, which cannot be properly described by the classical set theory which uses a simple true-false binary logic. By contrast, fuzzy-set theory has been shown to be a powerful tool for describing the multidimensionality and complexity of social phenomena. This book will be of significant interest to economists, statisticians and sociologists utilising quantitative methods to explore socio-economic phenomena.

Handbook of Empirical Economics and Finance explores the latest developments in the analysis and modeling of economic and financial data. Well-recognized econometric experts discuss the rapidly growing research in economics and finance and offer insight on the future direction of these fields. Focusing on micro models, the first group of chapters describes the statistical issues involved in the analysis of econometric models with cross-sectional data often arising in microeconomics. The book then illustrates time series models that are extensively used in empirical macroeconomics and finance. The last set of chapters explores the types of panel data and spatial models that are becoming increasingly significant in analyzing complex economic behavior and policy evaluations. This handbook brings together both background material and new methodological and applied results that are extremely important to the current and future frontiers in empirical economics and finance. It emphasizes inferential issues that transpire in the analysis of cross-sectional, time series, and panel data-based empirical models in economics, finance, and related disciplines.

The book's comprehensive coverage on the application of econometric methods to empirical analysis of economic issues is impressive. It uncovers the missing link between textbooks on economic theory and econometrics and highlights the powerful connection between economic theory and empirical analysis perfectly through examples on rigorous experimental design. The use of data sets for estimation derived with the Monte Carlo method helps facilitate the understanding of the role of hypothesis testing applied to economic models. Topics covered in the book are: consumer behavior, producer behavior, market equilibrium, macroeconomic models, qualitative-response models, panel data analysis and time-series analysis. Key econometric models are introduced, specified, estimated and evaluated. The treatment on methods of estimation in econometrics and the discipline of hypothesis testing makes it a must-have for graduate students of economics and econometrics and aids their understanding on how to estimate economic models and evaluate the results in terms of policy implications.

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. It is invaluable as a textbook for graduate-level courses and students or a handy reference for researchers and practitioners in financial mathematics and econometrics.

Discover the secrets to applying simple econometric techniques to improve forecasting Equipping analysts, practitioners, and graduate students with a statistical framework to make effective decisions based on the application of simple economic and statistical methods, Economic and Business Forecasting offers a comprehensive and practical approach to quantifying and accurate forecasting of key variables. Using simple econometric techniques, author John E. Silvia focuses on a select set of major economic and financial variables, revealing how to optimally use statistical software as a template to apply to your own variables of interest. * Presents the economic and financial variables that offer unique insights into economic performance * Highlights the econometric techniques that can be used to characterize variables * Explores the application of SAS software, complete with simple explanations of SAS-code and output * Identifies key econometric issues with practical solutions to those problems Presenting the "ten commandments" for economic and business forecasting, this book provides you with a practical forecasting framework you can use for important everyday business applications.

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.

When John Maynard Keynes likened Jan Tinbergen's early work in econometrics to black magic and alchemy, he was expressing a widely held view of a new discipline. However, even after half a century of practical work and theorizing by some of the most accomplished social scientists, Keynes' comments are still repeated today. This book assesses the foundations and development of econometrics and sets out a basis for the reconstruction of the foundations of econometric inference by examining the various interpretations of probability theory that underlie econometrics.