The ideal review for your statistics and econometrics course

More than 40 million students have trusted Schaum's Outlines for their expert knowledge and helpful solved problems. Written by renowned experts in their respective fields, Schaum's Outlines cover everything from math to science, nursing to language. The main feature for all these books is the solved problems. Step-by-step, authors walk readers through coming up with solutions to exercises in their topic of choice. Clear, concise explanations of all statistics and econometrics concepts Appropriate for the following courses: Statistics and Econometrics, Statistical Methods in Economics, Quantitative Methods in Economics, Mathematical Economics, Micro-Economics, Macro-Economics, Math for Economists, Math for Social Sciences

The State and Metropolitan Area Data Book is the continuation of the U.S. Census Bureau's discontinued publication. It is a convenient summary of statistics on the social and economic structure of the states, metropolitan areas, and micropolitan areas in the United States. It is designed to serve as a statistical reference and guide to other data publications and sources. This new edition features more than 1,500 data items from a variety of sources. It covers many key topical areas including population, birth and death rates, health coverage, school enrollment, crime rates, income and housing, employment, transportation, and government. The metropolitan area information is based on the latest set of definitions of metropolitan and micropolitan areas including: a complete listing and data for all states, metropolitan areas, including micropolitan areas, and their component counties 2010 census counts and more recent population estimates for all areas results of the 2016 national and state elections expanded vital statistics, communication, and criminal justice data data on migration and commuting habits American Community Survey 1- and 3-year estimates data on health insurance and housing and finance matters accurate and helpful citations to allow the user to directly consult the source source notes and explanations A guide to state statistical abstracts and state information Economic development officials, regional planners, urban researchers, college students, and data users can easily see the trends and changes affecting the nation today.

As one of the first texts to take a behavioral approach to macroeconomic expectations, this book introduces a new way of doing economics. Roetheli uses cognitive psychology in a bottom-up method of modeling macroeconomic expectations. His research is based on laboratory experiments and historical data, which he extends to real-world situations. Pattern extrapolation is shown to be the key to understanding expectations of inflation and income. The quantitative model of expectations is used to analyze the course of inflation and nominal interest rates in a range of countries and historical periods. The model of expected income is applied to the analysis of business cycle phenomena such as the great recession in the United States. Data and spreadsheets are provided for readers to do their own computations of macroeconomic expectations. This book offers new perspectives in many areas of macro and financial economics.

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

The papers in this volume analyze the deployment of Big Data to solve both existing and novel challenges in economic measurement. The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with administrative records generated in connection with tax administration. The increasing difficulty of obtaining survey and census responses threatens the viability of existing data collection approaches. The growing availability of new sources of Big Data-such as scanner data on purchases, credit card transaction records, payroll information, and prices of various goods scraped from the websites of online sellers-has changed the data landscape. These new sources of data hold the promise of allowing the statistical agencies to produce more accurate, more disaggregated, and more timely economic data to meet the needs of policymakers and other data users. This volume documents progress made toward that goal and the challenges to be overcome to realize the full potential of Big Data in the production of economic statistics. It describes the deployment of Big Data to solve both existing and novel challenges in economic measurement, and it will be of interest to statistical agency staff, academic researchers, and serious users of economic statistics.

Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) inference, as well as empirical Bayes procedures closely related to penalized likelihood estimation and mixed models, are considered here. Throughout, the focus is on semiparametric regression and smoothing based on basis expansions of unknown functions and effects in combination with smoothness priors for the basis coefficients. Beginning with a review of basic methods for smoothing and mixed models, longitudinal data, spatial data and event history data are treated in separate chapters. Worked examples from various fields such as forestry, development economics, medicine and marketing are used to illustrate the statistical methods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes. These, as well as some of the data sets, are made publicly available on the website accompanying this book.

This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the estimation motivated by minimizing quantile prediction error without assumption of true model. It provides the reader with a new horizon for understanding the prediction problem that occurs in time series modeling and a contemporary approach of hypothesis testing by the generalized empirical likelihood method. Nonparametric aspects of the methods proposed in this book also satisfactorily address economic and financial problems without imposing redundantly strong restrictions on the model, which has been true until now. Dealing with infinite variance processes makes analysis of economic and financial data more accurate under the existing results from the demonstrative research. The scope of applications, however, is expected to apply to much broader academic fields. The methods are also sufficiently flexible in that they represent an advanced and unified development of prediction form including multiple-point extrapolation, interpolation, and other incomplete past forecastings. Consequently, they lead readers to a good combination of efficient and robust estimate and test, and discriminate pivotal quantities contained in realistic time series models.

Dieses essential erklart das grundlegende Prinzip statistischer Testverfahren. Dabei stehen die Bedeutung der statistischen Signifikanz sowie des p-Wertes im Fokus. Haufig anzutreffende Fehlinterpretationen werden angesprochen. Dadurch wird ersichtlich, was ein signifikantes Ergebnis aussagt und, was es nicht aussagt. Der Leser wird somit befahigt, adaquat mit Testergebnissen umzugehen.

Das Arbeitsbuch stellt eine Aufgabensammlung mit detaillierten Loesungen zur Einfuhrung in die Angewandte Statistik fur Studenten zur Verfugung. Die Aufgaben umfassen dabei die Themengebiete, welche in etwa in drei Semestern Statistikausbildung gelehrt werden. Damit ist das Arbeitsbuch insbesondere fur Studierende der Wirtschaftswissenschaften, Humanmedizin, Psychologie, Ingenieurswissenschaften sowie Informatik von Interesse. Insgesamt wird durch interessante, teilweise reale und teilweise fiktive Sachverhalte das Lernen des ansonsten eher trockenen vermittelten Stoffes erleichtert. Praktische Aufgaben, die mithilfe der Statistiksoftware R geloest werden mussen, sind besonders gekennzeichnet. Am Ende des Buches gibt es Aufgaben zu gemischten Themengebieten zur Klausurvorbereitung.

'The Number Bias combines vivid storytelling with authoritative analysis to deliver a warning about the way numbers can lead us astray - if we let them.' TIM HARFORD Even if you don't consider yourself a numbers person, you are a numbers person. The time has come to put numbers in their place. Not high up on a pedestal, or out on the curb, but right where they belong: beside words. It is not an overstatement to say that numbers dictate the way we live our lives. They tell us how we're doing at school, how much we weigh, who might win an election and whether the economy is booming. But numbers aren't as objective as they may seem; behind every number is a story. Yet politicians, businesses and the media often forget this - or use it for their own gain. Sanne Blauw travels the world to unpick our relationship with numbers and demystify our misguided allegiance, from Florence Nightingale using statistics to petition for better conditions during the Crimean War to the manipulation of numbers by the American tobacco industry and the ambiguous figures peddled during the EU referendum. Taking us from the everyday numbers that govern our health and wellbeing to the statistics used to wield enormous power and influence, The Number Bias counsels us to think more wisely. 'A beautifully accessible exploration of how numbers shape our lives, and the importance of accurately interpreting the statistics we are fed.' ANGELA SAINI, author of Superior

The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.

Otto Opitz feiert im Juni 1999 seinen sechzigsten Geburtstag. Aus diesem Anlass haben sich Schuler, ihm nahestehende Kollegen und Freunde ent- schlossen, die nachfolgende Festschrift zu erstellen. Dass sich dabei eine hohe Korrelation zwischen den wissenschaftlichen Interessensgebieten von Otto Opitz und den Themen der eingegangenen Beitrage gezeigt hat, ist nicht er- staunlich und hat die Strukturierung dieses Bandes erleichtert. Ein Auszug seiner wissenschaftlichen Tatigkeiten findet sich am Ende des Bandes. Eines der wichtigsten Betatigungsfelder von Otto Opitz kann mit Daten- analyse und Klassifikation umschrieben werden. Und so, wie sich diese For- schungsrichtung aus der Statistik entwickelt hat, ist in jungster Zeit eine Diskussion zu beobachten, die Data Mining als neues Forschungsgebiet zu etablieren sucht. Zu bevorzugten Anwendungsbereichen von Aktivitaten aus den zuvor genannten Gebieten haben fur Otto Opitz stets Marktforschung und Marketing gehoert, und nicht nur seine Beschaftigung mit Methoden der Bankmarktforschung belegt, dass auch die mit Kapital und Risiko umschreib- bare Forschungsrichtung sein Interesse gefunden hat. Schon in fruhen Arbei- ten von Otto Opitz finden sich spieltheoretische UEberlegungen, die Aus- gangspunkt fur Forschungsaktivitaten in den Gebieten Operations Research und Unternehmensplanung sowie Volkswirtschaftslehre waren. Naturlich ha- ben Entwicklungen in der Informatik seine wissenschaftlichen Arbeiten be- pe-gestutzter Software zur Datenana- einflusst, wobei die Bereitstellung von lyse den Methodeneinsatz in der Lehre unterstutzt hat.

The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.

In recent years, interest in rigorous impact evaluation has grown tremendously in policy-making, economics, public health, social sciences and international relations. Evidence-based policy-making has become a recurring theme in public policy, alongside greater demands for accountability in public policies and public spending, and requests for independent and rigorous impact evaluations for policy evidence. Frölich and Sperlich offer a comprehensive and up-to-date approach to quantitative impact evaluation analysis, also known as causal inference or treatment effect analysis, illustrating the main approaches for identification and estimation: experimental studies, randomization inference and randomized control trials (RCTs), matching and propensity score matching and weighting, instrumental variable estimation, difference-in-differences, regression discontinuity designs, quantile treatment effects, and evaluation of dynamic treatments. The book is designed for economics graduate courses but can also serve as a manual for professionals in research institutes, governments, and international organizations, evaluating the impact of a wide range of public policies in health, environment, transport and economic development.

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

This publication provides updated statistics on a comprehensive set of economic, financial, social, and environmental measures as well as select indicators for the Sustainable Development Goals (SDGs). The report covers the 49 regional members of ADB. It discusses trends in development progress and the challenges to achieving inclusive and sustainable economic growth across Asia and the Pacific. This 53rd edition looks at how most economies in the region have bounced back to varying degrees from the COVID-19 pandemic. A gradual recovery of cyclical industries, the release of pent-up consumer demand, and increased confidence levels have contributed to developing Asia's economy. To put into practice the "leave no one behind" principle of the Sustainable Development Goals, detailed and informative data is crucial. The 2022 report features a special supplement, Mapping the Public Voice for Development-Natural Language Processing of Social Media Text Data, which explores how natural language processing techniques can be applied to social media text data to map public sentiment and inform development research and policy making.