This book includes discussions related to solutions of such tasks as: probabilistic description of the investment function; recovering the income function from GDP estimates; development of models for the economic cycles; selecting the time interval of pseudo-stationarity of cycles; estimating characteristics/parameters of cycle models; analysis of accuracy of model factors. All of the above constitute the general principles of a theory explaining the phenomenon of economic cycles and provide mathematical tools for their quantitative description. The introduced theory is applicable to macroeconomic analyses as well as econometric estimations of economic cycles.

Organizations can use the valuable tool of data envelopment analysis (DEA) to make informed decisions on developing successful strategies, setting specific goals, and identifying underperforming activities to improve the output or outcome of performance measurement. Strategic Performance Management and Measurement Using Data Envelopment Analysis highlights the advantages of using DEA as a tool to improve business performance and identify sources of inefficiency in public and private organizations. These recently developed theories and applications of DEA will be useful for policymakers, managers, and practitioners in the areas of sustainable development of our society including environment, agriculture, finance, and higher education sectors.

Economic theories can be expressed in words, numbers, graphs and symbols. The existing traditional economics textbooks cover all four methods, but the general focus is often more on writing about the theory and methods, with few practical examples. With an increasing number of universities having introduced mathematical economics at undergraduate level, Basic mathematics for economic students aims to fill this gap in the field. Basic mathematics for economic students begins with a comprehensive chapter on basic mathematical concepts and methods (suitable for self-study, revision or tutorial purposes) to ensure that students have the necessary foundation. The book is written in an accessible style and is extremely practical. Numerous mathematical economics examples and exercises are provided as well as fully worked solutions using numbers, graphs and symbols. Basic mathematics for economic students is aimed at all economics students. It focuses on quantitative aspects and especially complements the two highly popular theoretical economics textbooks Understanding microeconomics and Understanding macroeconomics, both written by Philip Mohr and published by Van Schaik.

The collection of chapters in Volume 43 Part B of Advances in Econometrics serves as a tribute to Professor M. Hashem Pesaran. Hashem is one of the most innovative, influential, and productive econometricians of his generation, with over 200 papers published in leading scientific journals to his credit along with highly influential books on both theoretical and applied topics, significantly pushing forward the frontiers of knowledge in econometrics and economics. Thanks to his profound and pioneering work on theoretical and empirical questions, the economics profession has gained a much better understanding of both the power and limitations of econometric analysis. Reflecting the diversity of Hashem's many contributions, this volume includes chapters on a wide variety of topics, including panel modelling, micro applications, and econometric methodology. The long list of topics includes studies analysing multiple treatment effects in panels, heterogeneity and aggregation, an exploration of the Orthogonal to Backwards Means (OBM) estimator, and an examination of potential reasons for anaemic productivity growth in Italy using recent dynamic heterogeneous panel data methods developed by Hashem Pesaran and his co-authors.

This is an essential how-to guide on the application of structural equation modeling (SEM) techniques with the AMOS software, focusing on the practical applications of both simple and advanced topics. Written in an easy-to-understand conversational style, the book covers everything from data collection and screening to confirmatory factor analysis, structural model analysis, mediation, moderation, and more advanced topics such as mixture modeling, censored date, and non-recursive models. Through step-by-step instructions, screen shots, and suggested guidelines for reporting, Collier cuts through abstract definitional perspectives to give insight on how to actually run analysis. Unlike other SEM books, the examples used will often start in SPSS and then transition to AMOS so that the reader can have full confidence in running the analysis from beginning to end. Best practices are also included on topics like how to determine if your SEM model is formative or reflective, making it not just an explanation of SEM topics, but a guide for researchers on how to develop a strong methodology while studying their respective phenomenon of interest. With a focus on practical applications of both basic and advanced topics, and with detailed work-through examples throughout, this book is ideal for experienced researchers and beginners across the behavioral and social sciences.

This is an essential how-to guide on the application of structural equation modeling (SEM) techniques with the AMOS software, focusing on the practical applications of both simple and advanced topics. Written in an easy-to-understand conversational style, the book covers everything from data collection and screening to confirmatory factor analysis, structural model analysis, mediation, moderation, and more advanced topics such as mixture modeling, censored date, and non-recursive models. Through step-by-step instructions, screen shots, and suggested guidelines for reporting, Collier cuts through abstract definitional perspectives to give insight on how to actually run analysis. Unlike other SEM books, the examples used will often start in SPSS and then transition to AMOS so that the reader can have full confidence in running the analysis from beginning to end. Best practices are also included on topics like how to determine if your SEM model is formative or reflective, making it not just an explanation of SEM topics, but a guide for researchers on how to develop a strong methodology while studying their respective phenomenon of interest. With a focus on practical applications of both basic and advanced topics, and with detailed work-through examples throughout, this book is ideal for experienced researchers and beginners across the behavioral and social sciences.

The collection in Volume 43 Part A of Advances in Econometrics serves as a tribute to Professor M. Hashem Pesaran. Hashem is one of the most innovative, influential, and productive econometricians of his generation, with over 200 papers published in leading scientific journals to his credit along with highly influential books on both theoretical and applied topics, significantly pushing forward the frontiers of knowledge in econometrics and economics. Thanks to his profound and pioneering work on theoretical and empirical questions, the economics profession has gained a much better understanding of both the power and limitations of econometric analysis. Consistent with Hashem's contributions, this volume comprises of chapters on a variety of topics covering prediction and macroeconomic modelling. The list of topics includes studies on Bayesian Quantile regression methods, forecasting implications from the economic impact of global warming, assessment of DSGE models, and parameter estimation in the presence of multiple breaks.

In a time of changing technology and cultural shifts, it is difficult to measure some aspects of the workforce. Education and the American Workforce brings together a comprehensive collection of employment and education information from federal statistical agencies. The Census Bureau is the leading source of quality data about the nation's people and economy. The Bureau of Labor Statistics (BLS) is the principal federal agency responsible for measuring labor market activity, working conditions, and price changes in the economy. Together, these agencies produce a wealth of information about the American workforce. This book includes information about the jobs that people hold, the occupations that they pursue, the industries where they work, and the education levels that people have attained. In addition to tables, each section also includes relevant figures and highlights of notable data. Some examples of interesting data found inside Education and the American Workforce include: *With no formal educational requirement and a median salary of $22,680, 4.5 million people are employed as retail salespersons, the most of any single occupation. Cashiers and food preparation/serving workers account for another 3.5 million each. There are 2.9 million registered nurses, the most numerous of occupations that require a bachelor's degree. *The biggest numeric decline is expected for Postal Service mail carriers, dropping by about 78,000 in ten years. When combined with other Postal Service occupations-such as clerks, sorters, postmasters, and others-a decline of 140,000 jobs is expected for the Postal Service. *Among the 75 largest counties, Bronx County, NY had the highest number of residents age 25 and over with less than a high school diploma at 29.4 percent while Montgomery County, PA had the lowest percentage at 6.2 percent. *Meanwhile, New York County, NY and Fairfax County, VA had the highest percentage of residents with a bachelor's degree or higher at 59.9 percent followed by Montgomery County, MD at 57.9 percent among the 75 largest counties. Nationally, between 2011 and 2015, 29.8 percent of the population had a bachelor's degree or higher.

This volume of Research on Economic Inequality contains research on how we measure poverty, inequality and welfare and how these measurements contribute towards policies for social mobility. The volume contains eleven papers, some of which focus on the uneven impact of the Covid-19 pandemic on poverty and welfare. Opening with debates on theoretical issues that lie at the forefront of the measurement of inequality and poverty literature, the first two chapters go on to propose new methods for measuring wellbeing and inequality in multidimensional categorical environments, and for measuring pro-poor growth in a Bayesian setting. The following three papers present theoretical innovations for measuring poverty and inequality, namely, in estimating the dynamic probability of being poor using a Bayesian approach, and when presented with ordinal variables. The next three chapters are contributions on empirical methods in the measurement of poverty, inclusive economic growth and mobility, with a focus on India, Israel and a unique longitudinal dataset for Chile. The volume concludes with three chapters exploring the impact of the Covid-19 pandemic as an economic shock on income and wealth poverty in EU countries and in an Argentinian city slum.

Businesses are collecting massive amounts of data every day as a way to better understand their processes, competition, and the markets they serve. This data can be used to increase organizational productivity and performance; however, is essential that organizations collecting large data sets have the tools available to them to fully understand the data they are collecting. Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics takes a critical look at methods for enhancing an organization's operations and day-to-day activities through the effective use of data. Focusing on a variety of applications of predictive analytics within organizations of all types, this critical publication is an essential resource for business managers, data scientists, graduate-level students, and researchers.

This book presents a broad range of statistical techniques to address emerging needs in the field of repeated measures. It also provides a comprehensive overview of extensions of generalized linear models for the bivariate exponential family of distributions, which represent a new development in analysing repeated measures data. The demand for statistical models for correlated outcomes has grown rapidly recently, mainly due to presence of two types of underlying associations: associations between outcomes, and associations between explanatory variables and outcomes. The book systematically addresses key problems arising in the modelling of repeated measures data, bearing in mind those factors that play a major role in estimating the underlying relationships between covariates and outcome variables for correlated outcome data. In addition, it presents new approaches to addressing current challenges in the field of repeated measures and models based on conditional and joint probabilities. Markov models of first and higher orders are used for conditional models in addition to conditional probabilities as a function of covariates. Similarly, joint models are developed using both marginal-conditional probabilities as well as joint probabilities as a function of covariates. In addition to generalized linear models for bivariate outcomes, it highlights extended semi-parametric models for continuous failure time data and their applications in order to include models for a broader range of outcome variables that researchers encounter in various fields. The book further discusses the problem of analysing repeated measures data for failure time in the competing risk framework, which is now taking on an increasingly important role in the field of survival analysis, reliability and actuarial science. Details on how to perform the analyses are included in each chapter and supplemented with newly developed R packages and functions along with SAS codes and macro/IML. It is a valuable resource for researchers, graduate students and other users of statistical techniques for analysing repeated measures data.

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.

This book, dedicated to Winfried Stute on the occasion of his 70th birthday, presents a unique collection of contributions by leading experts in statistics, stochastic processes, mathematical finance and insurance. The individual chapters cover a wide variety of topics ranging from nonparametric estimation, regression modelling and asymptotic bounds for estimators, to shot-noise processes in finance, option pricing and volatility modelling. The book also features review articles, e.g. on survival analysis.

State Profiles 2018: The Population and Economy of Each U.S. State provides a wealth of current, authoritative, and comprehensive data on key demographic and economic indicators for each U.S. state and the District of Columbia. Each state is covered by a compact standardized chapter that allows for easy comparisons and timely analysis between the states. A ten-page profile for each U.S. state plus the District of Columbia provides reliable, up-to-date information on a wide range of topics, including: population, labor force, income and poverty, government finances, crime, education, health insurance coverage, voting, marital status, migration, and more. If you want a single source of key demographic and economic data on each of the U.S. states, there is no other book like State Profiles. This book provides an overview of the U.S. economy which provides a framework for understanding the state information. This book is primarily useful for public, school, and college and university libraries, as well as for economic and sociology departments. However, anyone needing state-level information-students, state officials, investors, economic analysts, concerned citizens-will find State Profiles wealth of data and analysis absolutely essential! A LOOK AT THE STATES South Carolina once again had the highest rate of traffic fatalities in the U.S. in 2016, with 1.88 deaths per 100 million vehicle miles driven. In 2016, 16.6 of Texans did not have health insurance, making it the state with the highest percent of uninsured residents. At more than twice the national average, West Virginia had the highest rate of drug overdose deaths in 2016 (52.0 deaths per 100,000 residents) Of all the states, Utah had the highest percent of children in 2017, with 29.9 percent of its population under age 18. Maryland's 2016 median household income of $78,945 was the highest in the country, and its poverty rate of 9.7 percent was the 3rd lowest among the states.

In many disciplines of science it is vital to know the effect of a 'treatment' on a response variable of interest; the effect being known as the 'treatment effect'. Here, the treatment can be a drug, an education program or an economic policy, and the response variable can be an illness, academic achievement or GDP. Once the effect is found, it is possible to intervene to adjust the treatment and attain a desired level of the response variable. A basic way to measure the treatment effect is to compare two groups, one of which received the treatment and the other did not. If the two groups are homogenous in all aspects other than their treatment status, then the difference between their response outcomes is the desired treatment effect. But if they differ in some aspects in addition to the treatment status, the difference in the response outcomes may be due to the combined influence of more than one factor. In non-experimental data where the treatment is not randomly assigned but self-selected, the subjects tend to differ in observed or unobserved characteristics. It is therefore imperative that the comparison be carried out with subjects similar in their characteristics. This book explains how this problem can be overcome so the attributable effect of the treatment can be found. This book brings to the fore recent advances in econometrics for treatment effects. The purpose of this book is to put together various economic treatments effect models in a coherent fashion, make it clear which can be parameters of interest, and show how they can be identified and estimated under weak assumptions. The emphasis throughout the book is on semi- and non-parametric estimation methods, but traditional parametric approaches are also discussed. This book is ideally suited to researchers and graduate students with a basic knowledge of econometrics.

This book introduces a new approach in the field of macroeconomic inventory studies: the use of multivariate statistics to evaluate long-term characteristics of inventory investments in developed countries. By analyzing a 44-year period series of annual inventory change in percentage of GDP in a set of OECD countries, disclosing their relationship to growth, industry structure and alternative uses of GDP (fixed capital investments, foreign trade and consumption), it fills a gap in the economic literature. It is generally accepted that inventories play an important role in all levels of the economy. However, while there is extensive literature on micro- (and even item-) level inventory problems, macroeconomic inventory studies are scarce. Both the long-term processes of inventory formation and their correlation with other macroeconomic factors provide interesting conclusions about economic changes and policies in our immediate past, and present important insights for the future.

This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Cadiz (Spain) between June 11-16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid. The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference. The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers from around the globe, and contribute to the further development of the field.

This book fills an important gap in studies on D. D. Kosambi. For the first time, the mathematical work of Kosambi is described, collected and presented in a manner that is accessible to non-mathematicians as well. A number of his papers that are difficult to obtain in these areas are made available here. In addition, there are essays by Kosambi that have not been published earlier as well as some of his lesser known works. Each of the twenty four papers is prefaced by a commentary on the significance of the work, and where possible, extracts from technical reviews by other mathematicians.