This book covers topics in advanced econometrics that I have taught in graduate econometrics programs of the University of California at San Diego, Southern Methodist University, Dallas, the Netherlands Network of Quantitative Economics, Tinbergen Institute, and the Free University, Amsterdam. The selection of the topics is based on my personal interest in the subjects, as well as lack of availability of suitable textbooks in these areas.

Rather than providing an encyclopedic survey of the literature, I have chosen a presentation which fills the gap between intermediate statistics and econometrics (including linear time series analysis) and the level necessary to gain access to the recent econometric literature; in particular, the literature on nonlinear and nonparametric regression, and advanced time series analysis. The ultimate goal is to provide the student with tools for independent research in these areas. This book is particularly suitable for self-tuition, and may prove useful in a graduate course in mathematical statistics and advanced econometrics.

The first four chapters contain enough material to fill a half-semester graduate course in asymptotic theory and nonlinear inference if one skips some of the material involved, and a full semester course if not. In teaching such a half-semester course I usually skip the details of the proofs in chapter 2, and focus on the relations between the various modes of convergence only. Also, I usually skip the sections of chapter 2 and chapter 4 dealing with non-identically distributed samples, and only sketch the proof of the uniform law of large numbers for the i.i.d. case (theorem 2.5.7).