Dept of Economics Fall 2006

UNC-Chapel Hill

Eric Ghysels

Syllabus Econ 870 (273)

ADVANCED ECONOMETRICS

Meeting time and place:

M 9:00-9:50 AM, Gardner 308

W 9:00-10:50 AM, Gardner 308

Recitations:

F 9:00-9:50 AM, Gardner 308

How to reach me:

I will hold office hours on Tuesday from 2:00pm to 4:00pm in Gardner 208D.

My email address is eghysels@unc.edu

Prerequisites:

Economics 271 “Introduction to Econometric Theory”

Economics 272 “Econometrics”

Mathematics 147 “Linear Algebra”

Econ 271 and Math 147 have given you the statistical and mathematical tools necessary to understand this course. Econ 272 has made you familiar with the practice of econometrics using actual data sets. This course tackles the modern developments of econometrics, including statistical inference for nonlinear, nonparametric or dynamic models. This requires a mathematically rigorous treatment of asymptotic distribution theory.

Goals:

This course provides a firm understanding of why certain econometric methods work and tries to give students the background for developing new methods. This is the reason why the course contains proofs or outlines the proofs of many assertions, focusing on the role played by the assumptions with economic content while ignoring some regularity conditions which have little bearing on applied work. Complementary readings will be proposed to students interested in doing research in econometric theory. The data configurations we have in mind are large cross sections or time series. Almost all of the econometric theory we present is asymptotic, which means that it is exactly true only in the limit as the sample size tends to infinity.

Organization of the course:

Homework: two before the midterm exam and two after (30%). They require that you derive theoretical results from theorems proven in class. The focus will be not only on mathematical derivations, but also on discussion of modeling issues and relevance of assumptions.

Recitation with Mike Aguilar (maguilar@email.unc.edu) on Fridays.

Midterm on October 23rd (30%). The midterm will have the same format as the homework assignments.

Final about December 12^th (40%). The final will be cumulative, i.e. cover all the chapters since the beginning of the semester. The format is similar to homework and midterm. The grading will be numerical (the maximum grade being 100), which will then be converted to H, P, L or F.

Textbook and Readings:

Required:

Econometrics

by Bruce E. Hansen (University of Wisconsin).

This is a draft of an incomplete first-year Ph.D. econometrics textbook. This manuscript may be printed and reproduced for individual or instructional use, but may not be printed for commercial purposes.

Other useful books:

F. Hayashi (2000) “Econometrics”, Princeton University Press.

This book covers all the important topics in econometrics (except non-parametric estimation and simulation-based methods) in a succinct manner and contains nice economic examples, both in time-series and cross-section. The exposition is accessible to students who have a working knowledge of very basic linear algebra and probability theory.

J. Davidson (2000) “Econometric Theory”, Blackwell Publishers.

Table of contents similar to Hayashi, but with much more focus on the state-of-the-art in asymptotic distribution theory. This book is useful to get the details of the mathematical proofs and assumptions that are skipped in Hayashi.

Complementary:

H.J. Bierens (2004) “Introduction to the Mathematical and Statistical Foundations of Econometrics”, Cambridge University Press.

Thorough but self-contained treatment of the probability theory tools necessary for advanced econometrics.

A. Cameron and P. Trivedi (2005) “Microeconometrics”, Includes useful chapters on non-parametric estimation as well as simulation-based methods.

R. Davidson and J.G. MacKinnon ( 1993) “Estimation and Inference in Econometrics”, Oxford University Press.

Very discursive with an original and coherent approach based on artificial regressions. Includes a chapter on Monte Carlo methods.

Overview of the course:

Lecture 1: Linear Models

Hansen: Chapters 2, 3, 4