Which good econometrics textbooks would you recommend?
Edit: there are quite a few books out there, with varying levels of mathematical sophistication. It would be good to get some idea of how technical the book you're recommending is.
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I definitely do not like Kruschke's book. I read it, worked out a lot of examples, but didn't understand anything about bayesian inference! As an introductory book about bayesian statistics, you could better look at Scott Lynch, Introduction to Applied Bayesian Statistics and Estimation for Social Scientists, or at Tony Lancaster, An Introduction to Modern Bayesian Econometrics.
Wakefield's book is a good choice. But, how could I say?, he describes bayesian inference more than explaining it, because his point of view is a neutral one: "Each of the frequentist and Bayesian approaches have their merits and can often be used in tandem." I do not agree... But it's my own point of view :-) If you are going to read it, be prepared: the field is biostatistics (dental growth, prostate cancer, etc.) Moreover, you could be not interested in GEE (econometricians use GMM) or in nonparametric modeling. However, you can get a sense of Wakefield's book by looking at his courses. For example, http://courses.washington.edu/b571/lectures/
I would recommend two books if you are interested in smoothing techniques, especially in density estimation and regression (rather than in tests that don’t require classical normality assumptions, which are often based on ranks rather than the raw data):
- Nonparametric and Semiparametric Models by Härdle, Müller, Sperlich, and Werwatz
- Li and Racine's Nonparametric Econometrics: Theory and Practice
The first is much slimmer, a bit more introductory, with lots of examples and illustrations. It covers histograms, nonparametric density estimation, nonparametric regression, semiparametric and generalized regression models, single index models, generalized partial linear models, additive models and their marginal effects and generalized additive models.
The second tome covers nonparametric kernel methods, semiparametric methods, consistent model specification tests, nonparametric nearest neighbor and series methods, and some time series, simultaneous equations, and panel data models at the end. There is not too much about QR in this book. Koenker's QR would make a nice supplement.
It is also worth mentioning some other books. While comprehensive and worth reading later, I found Pagan and Ullah to be a difficult first introductions to this material. I have heard good things about Yatchew's Semiparametric Regression book, but I have not read it myself.
Best Answer
Definitively Econometric Analysis, by Greene. I'm not an econometrician, but I found this book very useful and well written.