This book treats the notion of morphisms in spatial analysis, paralleling these concepts in spatial statistics (Part I) and spatial econometrics (Part II). The principal concept is morphism (e.g., isomorphisms, homomorphisms, and allomorphisms), which is defined as a structure preserving the functional linkage between mathematical properties or operations in spatial statistics and spatial econometrics, among other disciplines. The purpose of this book is to present selected conceptions in both domains that are structurally the same, even though their labelling and the notation for their elements may differ. As the approaches presented here are applied to empirical materials in geography and economics, the book will also be of interest to scholars of regional science, quantitative geography and the geospatial sciences. It is a follow-up to the book "Non-standard Spatial Statistics and Spatial Econometrics" by the same authors, which was published by Springer in 2011.

This book presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017. It discusses topics in time series analysis and forecasting, including advanced mathematical methodology, computational intelligence methods for time series, dimensionality reduction and similarity measures, econometric models, energy time series forecasting, forecasting in real problems, online learning in time series as well as high-dimensional and complex/big data time series. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing computer science, mathematics, statistics and econometrics.

This book explains and explores the growth curve model as a tool to gain insights into various research topics of interest to academics and practitioners alike. It includes studies on growth models for repeated measurement mixture experiments, and optimal designs for growth prediction in order to find an optimum design for the most efficient estimation of the parameters of the mixture models. It presents longitudinal studies conducted on the mathematical aptitude and intelligence quotient of tribal population in North Eastern states of India, and innovative statistical analysis showing that the status of tribes is improving over time. These results are supplemented by similar cross- sectional studies, and a retrospective longitudinal study of the social environment in North Eastern tribes indicating that the growth status of the social environment is improving. Child health is an important topic in developing countries, and as such the book features an overview of the growth and nutritional status of children aged 5 to 18 in India. Characterization of Extended Uniform Distribution and its applications for quality control in industrial production, and in yield data of tuber crops among others are discussed. Characterizations of distribution in terms of performance rate are also proved. There is also a contribution examining the past and present status of mangroves in Sunderban region of the Indian state of West Bengal from an ecological viewpoint using a growth curve model set-up. Lastly, it includes a chapter on a statistical study of platelet size decomposition and related growth model. Highlighting the importance of growth curve modelling as it applies to actual field data and encouraging more theoretically inclined statisticians to look into theoretical issues that need investigation, the book disseminates applications of the growth curve model to real-world problems and addresses related theoretical issues for the attention of theoreticians and practitioners.

This practical guide in Eviews is aimed at practitioners and students in business, economics, econometrics, and finance. It uses a step-by-step approach to equip readers with a toolkit that enables them to make the most of this widely used econometric analysis software. Statistical and econometrics concepts are explained visually with examples, problems, and solutions. Developed by economists, the Eviews statistical software package is used most commonly for time-series oriented econometric analysis. It allows users to quickly develop statistical relations from data and then use those relations to forecast future values of the data. The package provides convenient ways to enter or upload data series, create new series from existing ones, display and print series, carry out statistical analyses of relationships among series, and manipulate results and output. This highly hands-on resource includes more than 200 illustrative graphs and tables and tutorials throughout. Abdulkader Aljandali is Senior Lecturer at Coventry University in London. He is currently leading the Stochastic Finance Module taught as part of the Global Financial Trading MSc. His previously published work includes Exchange Rate Volatility in Emerging Markers, Quantitative Analysis, Multivariate Methods & Forecasting with IBM SPSS Statistics and Multivariate Methods and Forecasting with IBM (R) SPSS (R) Statistics. Dr Aljandali is an established member of the British Accounting and Finance Association and the Higher Education Academy. Motasam Tatahi is a specialist in the areas of Macroeconomics, Financial Economics, and Financial Econometrics at the European Business School, Regent's University London, where he serves as Principal Lecturer and Dissertation Coordinator for the MSc in Global Banking and Finance at The European Business School-London.

This textbook addresses postgraduate students in applied mathematics, probability, and statistics, as well as computer scientists, biologists, physicists and economists, who are seeking a rigorous introduction to applied stochastic processes. Pursuing a pedagogic approach, the content follows a path of increasing complexity, from the simplest random sequences to the advanced stochastic processes. Illustrations are provided from many applied fields, together with connections to ergodic theory, information theory, reliability and insurance. The main content is also complemented by a wealth of examples and exercises with solutions.

This book is a quarterly forecast and analysis report on the Chinese economy. It is published twice a year and presents ongoing results from the "China Quarterly Macroeconomic Model (CQMM)," a research project at the Center for Macroeconomic Research (CMR) at Xiamen University. Based on the CQMM, the research team forecasts China's major macroeconomic indicators for the next 8 quarters, including GDP growth rate, CPI, PPI, investment in fixed assets, household consumption, imports, exports, and foreign reserves. Moreover, it simulates different scenarios to study the effects of macroeconomic policy on the Chinese economy. In addition to helping readers to understand China's economic trends and policies, this book has three main goals: to help readers understand China's economic performance; to forecast the major macroeconomic indicators for the next 8 quarters; and to simulate the effectiveness of macroeconomic policy.

Written in honor of Emeritus Professor Georges Prat (University of Paris Nanterre, France), this book includes contributions from eminent authors on a range of topics that are of interest to researchers and graduates, as well as investors and portfolio managers. The topics discussed include the effects of information and transaction costs on informational and allocative market efficiency, bubbles and stock price dynamics, paradox of rational expectations and the principle of limited information, uncertainty and expectation hypotheses, oil price dynamics, and nonlinearity in asset price dynamics.

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

This book reviews recent approaches for partial identification of average treatment effects with instrumental variables in the program evaluation literature, including Manski's bounds, bounds based on threshold crossing models, and bounds based on the Local Average Treatment Effect (LATE) framework. It compares these bounds across different sets of assumptions, surveys relevant methods to assess the validity of these assumptions, and discusses estimation and inference methods for the bounds. The book also reviews some empirical applications employing bounds in the program evaluation literature. It aims to bridge the gap between the econometric theory on which the different bounds are based and their empirical application to program evaluation.

"It's the economy, stupid," as Democratic strategist James Carville would say. After many years of study, Ray C. Fair has found that the state of the economy has a dominant influence on national elections. Just in time for the 2012 presidential election, this new edition of his classic text, "Predicting Presidential Elections and Other Things," provides us with a look into the likely future of our nation's political landscape--but Fair doesn't stop there.
Fair puts other national issues under the microscope as well--including congressional elections, Federal Reserve behavior, and inflation. In addition he covers topics well beyond today's headlines, as the book takes on questions of more direct, personal interest such as wine quality, predicting football games, and aging effects in baseball. Which of your friends is most likely to have an extramarital affair? How important is class attendance for academic performance in college? How fast can you expect to run a race or perform some physical task at age 55, given your time at age 30? Read "Predicting Presidential Elections and Other Things" and find out
As Fair works his way through an incredibly broad range of questions and topics, he teaches and delights. The discussion that underlies each chapter topic moves from formulating theories about real world phenomena to lessons on how to analyze data, test theories, and make predictions. At the end of this book, readers will walk away with more than mere predictions. They will have learned a new approach to thinking about many age-old concerns in public and private life, and will have a myriad of fun facts to share.

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.

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 presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017. It discusses topics in time series analysis and forecasting, including advanced mathematical methodology, computational intelligence methods for time series, dimensionality reduction and similarity measures, econometric models, energy time series forecasting, forecasting in real problems, online learning in time series as well as high-dimensional and complex/big data time series. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing computer science, mathematics, statistics and econometrics.

A unique and comprehensive source of information, this book is the only international publication providing economists, planners, policymakers and business people with worldwide statistics on current performance and trends in the manufacturing sector. The Yearbook is designed to facilitate international comparisons relating to manufacturing activity and industrial development and performance. It provides data which can be used to analyse patterns of growth and related long term trends, structural change, and industrial performance in individual industries. Statistics on employment patterns, wages, consumption and gross output and other key indicators are also presented.

Designed for a one-semester course, Applied Statistics for Business and Economics offers students in business and the social sciences an effective introduction to some of the most basic and powerful techniques available for understanding their world. Numerous interesting and important examples reflect real-life situations, stimulating students to think realistically in tackling these problems. Calculations can be performed using any standard spreadsheet package. To help with the examples, the author offers both actual and hypothetical databases on his website http://iwu.edu/~bleekley The text explores ways to describe data and the relationships found in data. It covers basic probability tools, Bayes' theorem, sampling, estimation, and confidence intervals. The text also discusses hypothesis testing for one and two samples, contingency tables, goodness-of-fit, analysis of variance, and population variances. In addition, the author develops the concepts behind the linear relationship between two numeric variables (simple regression) as well as the potentially nonlinear relationships among more than two variables (multiple regression). The final chapter introduces classical time-series analysis and how it applies to business and economics. This text provides a practical understanding of the value of statistics in the real world. After reading the book, students will be able to summarize data in insightful ways using charts, graphs, and summary statistics as well as make inferences from samples, especially about relationships.

This textbook addresses postgraduate students in applied mathematics, probability, and statistics, as well as computer scientists, biologists, physicists and economists, who are seeking a rigorous introduction to applied stochastic processes. Pursuing a pedagogic approach, the content follows a path of increasing complexity, from the simplest random sequences to the advanced stochastic processes. Illustrations are provided from many applied fields, together with connections to ergodic theory, information theory, reliability and insurance. The main content is also complemented by a wealth of examples and exercises with solutions.

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

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.

This introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital. The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics, macroeconomics, etc. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R as well as supplementary material that will enable the reader to quickly adapt all methods to their own applications.

This volume focuses on the analysis and measurement of business cycles in Brazil, Russia, India, China and South Africa (BRICS). Divided into five parts, it begins with an overview of the main concepts and problems involved in monitoring and forecasting business cycles. Then it highlights the role of BRICS in the global economy and explores the interrelatedness of business cycles within BRICS. In turn, part two provides studies on the historical development of business cycles in the individual BRICS countries and describes the driving forces behind those cycles. Parts three and four present national business tendency surveys and composite cyclical indices for real-time monitoring and forecasting of various BRICS economies, while the final part discusses how the lessons learned in the BRICS countries can be used for the analysis of business cycles and their socio-political consequences in other emerging countries.

This book explores the US economy from 1960 to 2010 using a more Keynsian, Cowles model approach, which the author argues has substantial advantages over the vector autoregression (VAR) and dynamic stochastic general equilibrium (DSGE) models used almost exclusively today. Heim presents a robust argument in favor of the Cowles model as an answer to the pressing, unresolved methodological question of how to accurately model the macroeconomy so that policymakers can reliably use these models to assist their decision making. Thirty-eight behavioral equations, describing determinants of variables such as consumption, taxes, and government spending, are connected by eighteen identities to construct a comprehensive model of the real US economy that Heim then tests across four different time periods to ensure that results are consistent. This comprehensive demonstration of the value of a long-ignored model provides overwhelming evidence that the more Keynesian (Cowles) structural models outperform VAR and DSGE, and therefore should be the models of choice in future macroeconomic studies.

This book studies the evolution of the middle class in Russia after the fall of the Soviet Union. Using data from the RLMS (Russian Longitudinal Monitoring Survey), the volume covers the period of transition (1991-2008) during which many fundamental economic reforms were implemented. The first part of the book is devoted to a discussion of the concept of middle class and a description of the economic situation in Russia during the transition period. Particular attention is given to variations in the distribution of Russian incomes and the estimated importance of the middle class. The second part of the book focuses on the link between the middle class and income bipolarization. The third and last section of the book uses the semiparametric "mixture model" to discover how many different groups may be derived from the income distribution in Russia, as well as what the main socio-economic and demographic characteristics of those groups are. The mobility of households into and out of the middle class during the transition period is also studied in hopes of determining the factors that contribute to such mobility. Using rigorous empirical methods, this volume sheds light on a relatively unstudied economic group and provides insight for countries which are about to enter a transition period. As such, this book will be of great interest to researchers in economics and inequality as well as professionals and practitioners working with international organizations.

The interaction between mathematicians, statisticians and econometricians working in actuarial sciences and finance is producing numerous meaningful scientific results. This volume introduces new ideas, in the form of four-page papers, presented at the international conference Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), held at Universidad Carlos III de Madrid (Spain), 4th-6th April 2018. The book covers a wide variety of subjects in actuarial science and financial fields, all discussed in the context of the cooperation between the three quantitative approaches. The topics include: actuarial models; analysis of high frequency financial data; behavioural finance; carbon and green finance; credit risk methods and models; dynamic optimization in finance; financial econometrics; forecasting of dynamical actuarial and financial phenomena; fund performance evaluation; insurance portfolio risk analysis; interest rate models; longevity risk; machine learning and soft-computing in finance; management in insurance business; models and methods for financial time series analysis, models for financial derivatives; multivariate techniques for financial markets analysis; optimization in insurance; pricing; probability in actuarial sciences, insurance and finance; real world finance; risk management; solvency analysis; sovereign risk; static and dynamic portfolio selection and management; trading systems. This book is a valuable resource for academics, PhD students, practitioners, professionals and researchers, and is also of interest to other readers with quantitative background knowledge.

This is the second volume in a two-part series on frontiers in regional research. It identifies methodological advances as well as trends and future developments in regional systems modelling and open science. Building on recent methodological and modelling advances, as well as on extensive policy-analysis experience, top international regional scientists identify and evaluate emerging new conceptual and methodological trends and directions in regional research. Topics such as dynamic interindustry modelling, computable general equilibrium models, exploratory spatial data analysis, geographic information science, spatial econometrics and other advanced methods are the central focus of this book. The volume provides insights into the latest developments in object orientation, open source, and workflow systems, all in support of open science. It will appeal to a wide readership, from regional scientists and economists to geographers, quantitatively oriented regional planners and other related disciplines. It offers a source of relevant information for academic researchers and policy analysts in government, and is also suitable for advanced teaching courses on regional and spatial science, economics and political science.

This volume is a collection of methodological developments and applications of simulation-based methods that were presented at a workshop at Louisiana State University in November, 2009. The first two papers are extensions of the GHK simulator: one reconsiders the computation of the probabilities in a discrete choice model while another example uses an adaptive version of sparse-grids integration (SGI) instead of simulation. Two studies are focused specifically on the methodology: the first compares the performance of the maximum-simulated likelihood (MSL) approach with a proposed composite marginal likelihood (CML) approach in multivariate ordered-response situations, while the second examines methods of testing for the presence of heterogeneity in the heterogeneity model. Further topics examined include: education savings accounts, parent contributions and education attainment; estimating the effect of exchange rate flexibility on financial account openness; estimating a fractional response model with a count endogenous regressor; and modelling and forecasting volatility in a bayesian approach.