Microeconometrics with R

Auteur

Yves Croissant is Professor of Economics at the University Lumière Lyon-2. His main research interests are microeconometrics and transport economics.

Texte du rabat

This book is about doing microeconometrics which has become increasingly popular in the last decades, thanks to the availability of many individual data sets and to the development of computer performance.

Résumé

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

Contenu

Preface Part 1. The ordinary least square estimator 1. Simple linear regression model 2. Statistical properties of the simple linear estimator 3. Multiple Regression Model 4. Interpretation of the Coefficients Part 2. Beyond the OLS estimator 5. Maximum likelihood estimator 6. Non-spherical disturbances 7. Endogeneity 8. Treatment Effect 9. Spatial econometrics Part 3. Special responses 10. Binomial models 11. Censored and truncated models 12. Count data 13. Duration models 14. Discrete choice models References Indexes