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Applied Econometrics with R

Applied Econometrics with R Author Christian Kleiber
ISBN-10 0387773185
Release 2008-12-10
Pages 222
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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.



Applied Econometrics with R

Applied Econometrics with R Author Christian Kleiber
ISBN-10 0387773169
Release 2008-08-28
Pages 222
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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.



Analysis of Integrated and Cointegrated Time Series with R

Analysis of Integrated and Cointegrated Time Series with R Author Bernhard Pfaff
ISBN-10 9780387759678
Release 2008-09-03
Pages 190
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This book is designed for self study. The reader can apply the theoretical concepts directly within R by following the examples.



Hands on Intermediate Econometrics Using R

Hands on Intermediate Econometrics Using R Author Hrishikesh D. Vinod
ISBN-10 9789812818850
Release 2008
Pages 512
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This book explains how to use R software to teach econometrics by providing interesting examples, using actual data applied to important policy issues. It helps readers choose the best method from a wide array of tools and packages available. The data used in the examples along with R program snippets, illustrate the economic theory and sophisticated statistical methods extending the usual regression. The R program snippets are not merely given as black boxes, but include detailed comments which help the reader better understand the software steps and use them as templates for possible extension and modification.



Principles of Econometrics

Principles of Econometrics Author Neeraj R Hatekar
ISBN-10 9788132106609
Release 2010-11-01
Pages 464
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This textbook makes learning the basic principles of econometrics easy for undergraduate and postgraduate students of economics. It specifically caters to the syllabus of ‘Introductory Econometrics’ course taught in the third year of the Bachelor of Economics programme in many universities. Principles of Econometrics takes the readers step-by-step from introduction to understanding, first introducing the basic statistical tools like concepts of probability, statistical distributions and hypothesis tests, and then going on to explain the two variable linear regression models along with certain additional tools such as the use of dummy variables and various data transformations. The most innovative feature of this textbook is that it familiarizes students with the role of R, which is a flexible and popular programming language. Using R, students will be able to implement a linear regression model and deal with the associated problems with substantial confidence.



Using R for Principles of Econometrics

Using R for Principles of Econometrics Author Constantin Colonescu
ISBN-10 9781387473618
Release 2018-01-05
Pages 276
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This is a beginner's guide to applied econometrics using the free statistics software R. It provides and explains R solutions to most of the examples in 'Principles of Econometrics' by Hill, Griffiths, and Lim, fourth edition. 'Using R for Principles of Econometrics' requires no previous knowledge in econometrics or R programming, but elementary notions of statistics are helpful.



Using R for Introductory Econometrics

Using R for Introductory Econometrics Author Florian Heiss
ISBN-10 1523285133
Release 2016-02-05
Pages 354
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Introduces the popular, powerful and free programming language and software package R Focus: implementation of standard tools and methods used in econometrics Compatible with "Introductory Econometrics" by Jeffrey M. Wooldridge in terms of topics, organization, terminology and notation Companion website with full text, all code for download and other goodies Praise: "A very nice resource for those wanting to use R in their introductory econometrics courses." (Jeffrey M. Wooldridge) Using R for Introductory Econometrics is a fabulous modern resource. I know I'm going to be using it with my students, and I recommend it to anyone who wants to learn about econometrics and R at the same time." (David E. Giles in his blog "Econometrics Beat") Topics: A gentle introduction to R Simple and multiple regression in matrix form and using black box routines Inference in small samples and asymptotics Monte Carlo simulations Heteroscedasticity Time series regression Pooled cross-sections and panel data Instrumental variables and two-stage least squares Simultaneous equation models Limited dependent variables: binary, count data, censoring, truncation, and sample selection Formatted reports and research papers combining R with R Markdown or LaTeX



Applied Econometrics

Applied Econometrics Author Dimitrios Asteriou
ISBN-10 9781137415479
Release 2015-10-12
Pages 552
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The third edition of Applied Econometrics builds on the success of the popular previous editions. It takes an intuitive, hands-on approach to presenting fundamental concepts in modern econometrics and carefully guides the reader through them. Step-by-step instructions for all econometric tests and methods of estimation are provided, as well as ways in which to interpret the results. This makes it an ideal companion for students new to the subject, or for those requiring a 'refresher'. Applied Econometrics third edition includes: • Thorough updates of all material in the book • More finance applications • A brand new Chapter 20: Time Varying Coefficient Models: A new way of estimating bias free parameters This is an indispensable textbook for undergraduate and Master's economics or finance students taking a course in applied econometrics.



A Primer for Spatial Econometrics

A Primer for Spatial Econometrics Author G. Arbia
ISBN-10 9781137317940
Release 2014-06-30
Pages 230
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This book aims at meeting the growing demand in the field by introducing the basic spatial econometrics methodologies to a wide variety of researchers. It provides a practical guide that illustrates the potential of spatial econometric modelling, discusses problems and solutions and interprets empirical results.



Econometric Analysis of Count Data

Econometric Analysis of Count Data Author Rainer Winkelmann
ISBN-10 354078389X
Release 2008-03-07
Pages 320
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The book provides an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed. Finally, applications are reviewed in various fields.



Applied Spatial Data Analysis with R

Applied Spatial Data Analysis with R Author Roger S. Bivand
ISBN-10 9781461476184
Release 2013-06-21
Pages 405
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Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.



The R Book

The R Book Author Michael J. Crawley
ISBN-10 9781118448960
Release 2012-11-07
Pages 1080
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Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)



Applied Nonparametric Econometrics

Applied Nonparametric Econometrics Author Daniel J. Henderson
ISBN-10 9781107010253
Release 2015-01-19
Pages 380
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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.



Data Science in R

Data Science in R Author Deborah Nolan
ISBN-10 9781482234824
Release 2015-04-21
Pages 539
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Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book’s collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data. Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers’ computational reasoning of real-world data analyses.



Wavelet Methods in Statistics with R

Wavelet Methods in Statistics with R Author Guy Nason
ISBN-10 9780387759616
Release 2010-07-25
Pages 259
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This book contains information on how to tackle many important problems using a multiscale statistical approach. It focuses on how to use multiscale methods and discusses methodological and applied considerations.



Handbook of Applied Econometrics Volume I Macroeconomics

Handbook of Applied Econometrics Volume I  Macroeconomics Author M Hashem Pesaran
ISBN-10 0631215581
Release 1999-06-25
Pages 482
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This is the first and most authoritative overview of the applied work that is happening in the field making it an essential resource for academics, researchers, graduate students and professional economists in industry and government.



Introductory Time Series with R

Introductory Time Series with R Author Paul S.P. Cowpertwait
ISBN-10 9780387886985
Release 2009-05-28
Pages 256
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This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.