Panel Data Econometrics with R – Yves Croissant, Giovanni Millo – 1st Edition

Description

Panel Data with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.

This book serves as a tutorial for using R in the field of panel data econometrics, illustrated throughout with examples in econometrics, political science, agriculture and ecology. It presents classic methodology and applications as well as more and recent developments in this field including spatial panels, dynamic and generalised models. Software are presented at a basic level to provide access to the casual R users. More advanced users will appreciate the scalability of the software and find directions towards more sophisticated operation, exploiting the functional nature of R and its object-orientation features.

Key features:
– Panel data is explained in a rigorous but practical way.
– A rich collection of fully reproducible examples features throughout.
– Written by the authors of the well-known ‘plm’ package for R.
– Covers the basics of panel data as taught in graduate econometrics classes.
– Includes datasets with examples in econometrics, political science, agriculture and ecology.
– Supported by accompanying website featuring R code, examples, replicable material and instructor materials.

Enriched by a wide collection of examples, Panel Data with R can be a practical companion to advanced textbooks as well as a primary text for practitioners. The techniques covered will also appeal to many social and natural scientists beside economists.

View more
  • Preface
    1. Introduction
    2. The Error Component Model
    3. Advanced Error Components Models
    4. Tests on Error Component Models
    5. Robust Inference and Estimation for Non-spherical Errors
    6. Endogeneity
    7. Estimation of a Dynamic Model
    8. Panel Time Series
    9. Count Data and Limited Dependent Variables
    10. Spatial Panels

    Bibliography
    Index
  • Citation

Leave us a commentNo Comments


guest
0 Comments
Inline Feedbacks
View all comments
0
Would love your thoughts, please comment.x
()
x