Economics 628 - Econometrics I

Instructor: Peter Fuleky
Office: 508 Saunders Hall
Email: fuleky at

Class Info:
Semester: Spring 2017
Location: SAUND 541
Time: MW 1:30-2:45pm


This course aims to provide first year economics Ph.D. students with an introduction to probability theory and statistical inference as well as modern econometric methods and theory.

- Probability Lecture Notes by Arne Hallam
- A Short Introduction to Probability by Dirk Kroese
- Introduction to Probability Notes by Charles Grinstead and Laurie Snell
- Introduction to Probability Notes by Dimitri Bertsekas and John Tsitsiklis
- A Modern Introduction to Probability and Statistics: Understanding Why and How by Dekking, Kraaikamp, Lopuhaa, and Meester
- Statistical Inference by George Casella and Roger L. Berger, 2nd Edition

- Probability Theory and Statistical Inference: Econometric Modeling with Observational Data by Aris Spanos

- Econometrics Lecture Notes by Bruce Hansen
- Econometrics Lecture Notes by Michael Creel
- A Primer in Econometric Theory by John Stachurski
- A Guide to Econometrics by Peter Kennedy, 6th Edition
- Introduction to Econometrics by Christopher Dougherty, 4th Edition
- Econometric Theory and Methods by Russell Davidson and James G. MacKinnon (required)

- A (Very) Short Introduction to R by Paul Torfs and Claudia Brauer
- R Reference Card
- R Reference Card 2.0
- Introduction to Probability and Statistics Using R by Jay Kerns
- Introduction to Statistical Thought by Michael Lavine
- Econometrics in R by Grant V. Farnsworth
- R Downloads and Documentation

Course Structure:
There will be two lectures per week. Attendance is not mandatory at lectures, but exams and homework assignments will focus primarily on material presented in lectures. Homework problems and answers to homework problems, as well as important information with regard to exams and announcements will be posted on the course website. It is your responsibility to check the website for announcements, assignments, and any possible changes related to the course plan. Grades will be determined by one midterm (40%) and one final exam (40%), as well as approximately weekly problem sets (20%).

Rough Outline of Topics:
1) Probability Theory
- Random Variables, Distribution Functions
- Expectations, Transformations
- Common Distributions
- Joint Distributions, Conditional Distributions, Covariance, Correlation
- Convergence, Central Limit Theorem
2) Econometrics
- Ordinary Least Squares Modeling and Estimation
- Ordinary Least Squares Asymptotics
- Maximum Likelihood
- Heteroskedasticity
- Serial Correlation
- Instrumental Variables
- Generalized Method of Moments

Econometrics: A Bird's Eye View
Intro to R script 1
Intro to R script 2
More illustration of concepts in R
Clickable distribution chart
Distribution relationships
R code for bivariate normal distro
Use of logarithms in economics
Linearly independent, orthogonal, and uncorrelated variables
Visualizing multiple regression
More on Venn diagrams for regression
A primer on asymptotics
Type II error and the power of a test
UH library's guide to economics