Applied Nonparametric Econometrics
[This article was first published on Econometrics Beat: Dave Giles' Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Recently, I received a copy of a new econometrics book, Applied Nonparametric Econometrics, by Daniel Henderson and Christopher Parmeter.
The title is pretty self-explanatory and, as you’d expect with any book published by CUP, this is a high-quality item.
The book’s Introduction begins as follows:
“The goal of this book is to help bridge the gap between applied economists and theoretical econometricians/statisticians. The majority of empirical research in economics ignores the potential benefits of nonparametric methods and many theoretical nonparametric advances ignore the problems faced by practitioners. We do not believe that applied economists dismiss these methods because they do not like them. We believe that they do not employ them because they do not understand how to use them or lack formal training on kernel smoothing.”
The authors provide a very readable, but careful, treatment of the main topics in nonparamteric econometrics, and a feature of this book is the set of empirical examples. The book’s website provides the data that are used (for replication purposes), as well as a number of routines in R. The latter provide a useful supplement to what is available already in the np package for R (Hayfield and Racine, 2008).
Reference
Hayfield T. and J. S. Racine, 2008. Nonparametric econometrics: The np package. Journal of Statistical Software, 27 (5), 1-32.
To leave a comment for the author, please follow the link and comment on their blog: Econometrics Beat: Dave Giles' Blog.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.