ECON 501 (Fall 2002)

Introduction to Statistics and Econometrics
for Ph.D. Students in Economics

Instructor:
Prof. Herman J. Bierens
Tel.: 865-4921, email: hbierens@psu.edu
Office hours: Wednesday 2-4 PM in 510 Kern.

T.A.:
Guang Guo
Office hours M W 4:30-6 PM in 404 Kern

Time and place: Tuesday & Thursday 1-2.15 PM in 202 Rackley

The objective of this course is to prepare the first year Ph.D. students in economics for the study of econometrics, by providing a rigorous introduction to probability and measure theory and mathematical statistics. Each week a number of exercises from the text books and/or lecture notes will be assigned as homework.

The final grade will be determined by the homework (20%), a written closed-book mid-term exam (40%), and a written closed-book final exam (40%). The final exam will cover the material in the mid-term exam as well. If you score higher on the final exam than on the mid-term exam, the latter score will be ignored, and the final exam will count for 80% of the final grade, provided that you have done your homework.

As to the homework, the two lowest scores will be ignored for the grade. If you do not turn a homework, the score for this homework assignment will be zero.

Required textbook

[RG] A. Ronald Gallant, An Introduction to Econometric Theory, Princeton University Press.

However, I will teach most of the material in this course on the basis of my own textbook (in progress), entitled: Introduction to the Mathematical and Statistical Foundations of Econometrics

Recommended textbook

[HC] R.V. Hogg and A.T. Craig, Introduction to Mathematical Statistics (Fifth Ed.), Macmillan. (You are not required to buy this book, but I recommend that you buy it anyhow. It is much easier to understand than Gallant's book, and therefore you may enhance your understanding of the chapters in Gallant's book by reading the corresponding chapters in Hogg & Craig first. Also, I may occasionally assign exercises from it.)

Topics

  1. Probability and measure. [Lecture notes; RG, Ch. 1]
  2. Borel measurable functions, integration with respect to probability measures, and mathematical expectations. [Lecture notes; RG, Ch.2-3]
  3. Conditional expectations. [Lecture notes; RG, Ch.2]
  4. Transformations of random variables and vectors. [Lecture notes; RG, Ch.3; HC, Ch. 4]
  5. Some important univariate continuous distributions [Lecture notes; RG, Ch. 3]
  6. The multivariate normal distribution. [Lecture notes; RG, Sec. 3.8; HC, Sec. 4.10, 10.8, 10.9]
  7. Modes of convergence. [Lecture notes; RG, Ch.4; HC, Ch. 5]
  8. Statistical inference, linear regression and maximum likelihood theory. [Lecture notes; RG, Ch.5; HC, Ch. 6 + 9] (if time permits)

Final Exam:

4:40 PM in 202 RACKLEY on Tuesday December 17, 2002.

Disability Message:

The Pennsylvania State University encourages qualified persons with disabilities to participate in its programs and activities. If you anticipate needing any type of accommodation in this course or have questions about physical access, please tell the instructor as soon as possible.