Learning Microeconometrics with R
1st Edition
Published on 30. December 2020
Book
Hardback
398 pages
978-0-367-25538-1 (ISBN)
Description
This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor's degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis.
Key Features:
Focuses on the assumptions underlying the algorithms rather than their statistical properties.
Presents cutting-edge analysis of factor models and finite mixture models.
Uses a hands-on approach to examine the assumptions made by the models and when the models fail to estimate accurately.
Utilizes interesting real-world data sets that can be used to analyze important microeconomic problems.
Introduces R programming concepts throughout the book.
Includes appendices that discuss some of the standard statistical concepts and R programming used in the book.
Key Features:
Focuses on the assumptions underlying the algorithms rather than their statistical properties.
Presents cutting-edge analysis of factor models and finite mixture models.
Uses a hands-on approach to examine the assumptions made by the models and when the models fail to estimate accurately.
Utilizes interesting real-world data sets that can be used to analyze important microeconomic problems.
Introduces R programming concepts throughout the book.
Includes appendices that discuss some of the standard statistical concepts and R programming used in the book.
Reviews / Votes
'The book offers a great set of statistical and econometric methods useful not only in microeconometrics but in many other fields where the statistical modeling is needed. At the same time, it is a great source of the R programming, particularly, in data simulation and solving complex problems. The whole volume is saturated with R scripts, examples of the language application to numerous aims, and each chapter suggests multiple links to the internet websites related to the topics under consideration. Bibliography of more than eighty sources is also supplied. The book presents an incredibly useful source of information on the modern statistical techniques and their implementations to solving various real-life problems, that makes it very helpful to students and researchers in their projects.'- Stan Lipovetsky, Technometrics, Vol. 63, Issue 3, August 2021 "...Very friendly and accessible in tone; informal in presentation. This book starts explaining code writing from the beginning. It is intended for graduate students interested and studying or researching in applied economics/econometrics. It provides all data sources with their corresponding links ... Two noticeable features in this book are practicality in applying econometrics and R codes for estimation and simulation of many examples in the book ... This book is very suitable as a textbook in applied economics/econometrics and adaptable for class teaching and a valuable resource for graduates." -Morteza-Alabaf Sabaghi, Journal of the Royal Statistical Society, Series A
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
72 s/w Abbildungen, 40 s/w Tabellen
40 Tables, black and white; 72 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 26 mm
Weight
763 gr
ISBN-13
978-0-367-25538-1 (9780367255381)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Christopher P. Adams
Learning Microeconometrics with R
E-Book
12/2020
1st Edition
Chapman & Hall/CRC
€125.99
Available for download
Christopher P. Adams
Learning Microeconometrics with R
E-Book
12/2020
1st Edition
Chapman & Hall/CRC
€125.99
Available for download
Person
Chris Adams was born and raised in Melbourne Australia and is a lifelong Carlton supporter. Chris received his PhD in economics from the University of Wisconsin - Madison. He has taught at the University of Vermont and in the Johns Hopkins' Masters of Applied Economics program. He has 17 years experience working in merger regulation and antitrust for the US Federal Trade Commission. He is currently a Principal Analyst at the US Congressional Budget Office. Chris' work and research focus on econometrics, empirical industrial organization, pharmaceutical innovation and auctions. His work has been published in various academic journals including The Econometrics Journal, Health Affairs, Health Economics, Marketing Science and Economics Letters. Chris is a colon cancer survivor and a research patient advocate with ECOG-ACRIN. Most importantly, he is a father to CJ and husband to Deena.
Content
I. Experiments.
1. Ordinary Least Squares.
2. Multiple Regression.
3. Instrumental Variables
4. Bounds Estimation.
II. Structural Estimation.
5. Estimating Demand.
6. Estimating Selection Models.
7. Demand Estimation with IV
8. Estimating Games.
9. Estimating Auction Models
III. Repeated Measurement.
10. Panel data.
11. Synthetic Controls.
12. Mixture Models.
IV. Appendices.
A. Measuring Uncertainty.
B. Statistical Programming in R
Acknowledgements
Bibliography
1. Ordinary Least Squares.
2. Multiple Regression.
3. Instrumental Variables
4. Bounds Estimation.
II. Structural Estimation.
5. Estimating Demand.
6. Estimating Selection Models.
7. Demand Estimation with IV
8. Estimating Games.
9. Estimating Auction Models
III. Repeated Measurement.
10. Panel data.
11. Synthetic Controls.
12. Mixture Models.
IV. Appendices.
A. Measuring Uncertainty.
B. Statistical Programming in R
Acknowledgements
Bibliography