The first cutting-edge guide to using the SAS® system for the analysis of econometric data Applied Econometrics Using the SAS® System is the first book of its kind to treat the analysis of basic econometric data using SAS®, one of the most commonly used software tools among today's statisticians in business and industry. This book thoroughly examines econometric methods and discusses how data collected in economic studies can easily be analyzed using the SAS® system. In addition to addressing the computational aspects of econometric data analysis, the author provides a statistical foundation by introducing the underlying theory behind each method before delving into the related SAS® routines. The book begins with a basic introduction to econometrics and the relationship between classical regression analysis models and econometric models. Subsequent chapters balance essential concepts with SAS® tools and cover key topics such as: Regression analysis using Proc IML and Proc Reg Hypothesis testing Instrumental variables analysis, with a discussion of measurement errors, the assumptions incorporated into the analysis, and specification tests Heteroscedasticity, including GLS and FGLS estimation, group-wise heteroscedasticity, and GARCH models Panel data analysis Discrete choice models, along with coverage of binary choice models and Poisson regression Duration analysis models Assuming only a working knowledge of SAS®, this book is a one-stop reference for using the software to analyze econometric data. Additional features include complete SAS® code, Proc IML routines plus a tutorial on Proc IML, and an appendix with additional programs and data sets. Applied Econometrics Using the SAS® System serves as a relevant and valuable reference for practitioners in the fields of business, economics, and finance. In addition, most students of econometrics are taught using GAUSS and STATA, yet SAS® is the standard in the working world; therefore, this book is an ideal supplement for upper-undergraduate and graduate courses in statistics, economics, and other social sciences since it prepares readers for real-world careers.

Using Applied Econometrics with SAS: Modeling Demand, Supply, and Risk, you will quickly master SAS applications for implementing and estimating standard models in the field of econometrics. This guide introduces you to the major theories underpinning applied demand and production economics. For each of its three main topics--demand, supply, and risk--a concise theoretical orientation leads directly into consideration of specific economic models and econometric techniques, collectively covering the following: Double-log demand systems Linear expenditure systems Almost ideal demand systems Rotterdam models Random parameters logit demand models Frequency-severity models Compound distribution models Cobb-Douglas production functions Translogarithmic cost functions Generalized Leontief cost functions Density estimation techniques Copula models SAS procedures that facilitate estimation of demand, supply, and risk models include the following, among others: PROC MODEL PROC COPULA PROC SEVERITY PROC KDE PROC LOGISTIC PROC HPCDM PROC IML PROC REG PROC COUNTREG PROC QLIM An empirical example, SAS programming code, and a complete data set accompany each econometric model, empowering you to practice these techniques while reading. Examples are drawn from both major scholarly studies and business applications so that professors, graduate students, government economic researchers, agricultural analysts, actuaries, and underwriters, among others, will immediately benefit.

Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable. This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs. This book is part of the SAS Press program.

This is the first book to present time series analysis using the SAS Enterprise Guide software. It includes some starting background and theory to various time series analysis techniques, and demonstrates the data analysis process and the final results via step-by-step extensive illustrations of the SAS Enterprise Guide software. This book is a practical guide to time series analyses in SAS Enterprise Guide, and is valuable resource that benefits a wide variety of sectors.

Applied Econometrics: A Practical Guide is an extremely user-friendly and application-focused book on econometrics. Unlike many econometrics textbooks which are heavily theoretical on abstractions, this book is perfect for beginners and promises simplicity and practicality to the understanding of econometric models. Written in an easy-to-read manner, the book begins with hypothesis testing and moves forth to simple and multiple regression models. It also includes advanced topics: Endogeneity and Two-stage Least Squares Simultaneous Equations Models Panel Data Models Qualitative and Limited Dependent Variable Models Vector Autoregressive (VAR) Models Autocorrelation and ARCH/GARCH Models Unit Root and Cointegration The book also illustrates the use of computer software (EViews, SAS and R) for economic estimating and modeling. Its practical applications make the book an instrumental, go-to guide for solid foundation in the fundamentals of econometrics. In addition, this book includes excerpts from relevant articles published in top-tier academic journals. This integration of published articles helps the readers to understand how econometric models are applied to real-world use cases.

Using Applied Econometrics with SAS: Modeling Demand, Supply, and Risk, you will quickly master SAS applications for implementing and estimating standard models in the field of econometrics. This guide introduces you to the major theories underpinning applied demand and production economics. For each of its three main topics—demand, supply, and risk—a concise theoretical orientation leads directly into consideration of specific economic models and econometric techniques, collectively covering the following: Double-log demand systems Linear expenditure systems Almost ideal demand systems Rotterdam models Random parameters logit demand models Frequency-severity models Compound distribution models Cobb-Douglas production functions Translogarithmic cost functions Generalized Leontief cost functions Density estimation techniques Copula models SAS procedures that facilitate estimation of demand, supply, and risk models include the following, among others: PROC MODEL PROC COPULA PROC SEVERITY PROC KDE PROC LOGISTIC PROC HPCDM PROC IML PROC REG PROC COUNTREG PROC QLIM An empirical example, SAS programming code, and a complete data set accompany each econometric model, empowering you to practice these techniques while reading. Examples are drawn from both major scholarly studies and business applications so that professors, graduate students, government economic researchers, agricultural analysts, actuaries, and underwriters, among others, will immediately benefit. This book is part of the SAS Press program.

This book will address such classification and econometrics techniques as cluster analysis, conjoint analysis, seemingly unrelated regression, and simultaneous equations modeling. The purpose and rationale for using each technique will be explained in lay man's terms, using illustrative concepts that are easily understood. Mathematical prerequisites are generally low; the author assumes her reader has some familiarity with descriptive statistics and multivariate analysis. After reading the book, the reader will be able to understand and apply each technique without having to know the meaning of Greek symbols and equations. The syntax and output for each technique will be discussed and the author will provide a clear explanation of how to interpret the output. Readers will know how to modify the syntax provided in the book and apply them to their own programs to use. Programming syntax in SPSS and R are provided.

R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you’ve identified it, and then how to successfully model that data. You’ll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today’s #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).