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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.
Why Care About Causation?
Covering the essential elements of the subject of econometrics, the author also introduces and explains techniques that are now widely used in applied work, although rarely introduced in detail in non-specialist texts, such as integrated time series, cointegration, simulation analysis, Johansen's Approach to multivariate co-integration and ARCH. The author explains the central distinction between stationary and nonstationary time series, which is of crucial importance in many areas of analysis, especially in macroeconomics and financial economics.
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.
Applied Econometrics takes an intuitive, hands-on approach to presenting modern econometrics. Wide-ranging yet compact, the book features extensive software integration and contains empirical applications throughout. It provides step-by-step guidelines for all econometric tests and methods of estimation, and also provides interpretations of the results. The second edition of this popular book features expanded topical coverage, more coverage of fundamental concepts for students new to the subject or requiring a "refresher", integrated finance applications throughout, as well as the addition of Stata to the software coverage (already featuring EViews and Microfit). New chapters include: - Limited Dependent Variable Regression Models - Identification in Standard and Cointegrated Systems - Solving Models This is an ideal book for undergraduate and master's economics or finance students taking a first course in applied econometrics. A companion website for this book is available at www.palgrave.com/economics/asteriou2 which contains: - Data files for students - PowerPoint slides for lecturers
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.
Fundamentals of Applied Econometrics is designed for an applied, undergraduate econometrics course providing students with an understanding of the most fundamental econometric ideas and tools. The text serves both the student whose interest is in understanding how one can use sample data to illuminate economic theory and the student who wants and needs a solid intellectual foundation on which to build practical experiential expertise. Divided into two parts, the first half provides a thorough undergraduate-level treatment of multiple regressions including an extensive statistics review with integrated, hands-on Acting Learning Exercises so students learn by doing. The second half of the book covers a number of advanced topics: panel data modeling, time series analysis, binary-choice modeling, and an introduction to GMM. This latter portion of the book is very suitable for a more advanced course: a second-term undergraduate course, a Masters level course, or as a companion reading for a Doctoral level course.
Taking a modern approach to the subject, this text provides students with a solid grounding in econometrics, using non-technical language wherever possible.
With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. Downloadable datasets are available online.