Microeconometrics With R
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Microeconometrics with R
This book is about doing microeconometrics, defined by Cameron and Trivedi (2005) as « the analysis of individual-level data on the economic behavior of individuals or firms using regression methods applied to cross-section and panel data » with R. Microeconometrics became increasingly popular in the last decades, thanks to the availability of many individual data sets and to the development of computer performance. R appeared in the late nineties as a clone of S. It became increasingly popular among statisticians, especially in fields where S was widely used. 20 years ago, using R for doing econometrics analysis required a lot of programming because a lot of core methods of econometrics were not available in R. Nowadays, most of the basic methods described in the book are available in contributed packages. Moreover, the set of packages called the tidyverse developed by Rstudio (now Posit) for all the basic tasks of an applied statistician (importing, tidying, transforming and visualizing data set) makes the use of R faster and easier. The book uses extensively specialized econometrics packages and the tidyverse and seeks to demonstrate that the adoption of R as the primary software for an econometrician is a relevant choice. The first part of the book is devoted to the ordinary least square estimator. Matrix algebra is progressively introduced in this part and a special attention is paid on the interpretation of the estimated coefficients. The second part goes beyond the basic OLS estimator by testing the hypothesis on which this estimator is based on and providing more complex estimators relevant when some of these hypotheses are violated. Finally, the third part of the book presents specific estimators devoted to « special » responses, eg count, binomial or duration data. Key Features: Many applications using data sets of recent academic works are developed Testing and estimation procedures using the programming framework of R and specialized packages are presented Two companion packages (micsr and micsr.data), containing respectively functions implementing some estimation and testing procedures not available in other contributed packages and data sets used in the book are provided
Microeconometrics with R
This book is about doing microeconometrics, defined by Cameron and Trivedi as "the analysis of individual-level data on the economic behavior of individuals or firms using regression methods applied to cross-section and panel data" with R. Microeconometrics became increasingly popular in the last decades, thanks to the availability of many individual data sets and to the development of computer performance. R appeared in the late nineties as a clone of S. It became increasingly popular among statisticians, especially in fields where S was widely used. Twenty years ago, using R for doing econometrics analysis required a lot of programming because a lot of core methods of econometrics were not available in R. Nowadays, most of the basic methods described in the book are available in contributed packages. Moreover, the set of packages called the tidyverse developed by RStudio (now Posit) for all the basic tasks of an applied statistician (importing, tidying, transforming and visualizing data sets) makes the use of R faster and easier. The book uses extensively specialized econometrics packages and the tidyverse, and it seeks to demonstrate that the adoption of R as the primary software for an econometrician is a relevant choice. The first part of the book is devoted to the ordinary least squares estimator. Matrix algebra is progressively introduced in this part, and special attention is paid to the interpretation of the estimated coefficients. The second part goes beyond the basic OLS estimator by testing the hypothesis on which this estimator is based and providing more complex estimators relevant when some of these hypotheses are violated. Finally, the third part of the book presents specific estimators devoted to "special" responses, e.g., count, binomial or duration data. Key Features: Many applications using data sets of recent academic works are developed Testing and estimation procedures using the programming framework of R and specialized packages are presented Two companion packages (micsr and micsr.data), containing respectively functions implementing some estimation and testing procedures not available in other contributed packages and data sets used in the book, are provided
Learning Microeconometrics with R
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.