Lecturer: Charlie Gibbons [homepage | e-mail]
Lecture: Mondays and Wednesdays, 5--6:30pm
Location: Giannini 201My office hours: After class (Giannini 234) or by appointment
The first day of class is Wednesday, August 26.
The take-home midterm will become available on Monday, October 26 at 5pm and will be due at the start of lecture on Wednesday, October 28.
Problem set 1 [solutions| R code | knitr]
Problem set 2 [solutions]
Problem set 3 [ solutions]
Problem set 4 [ solutions part 1 | solutions part 2]
Midterm exam [ solutions]
Problem set 5 [data | solutions]
Problem set 6 [solutions]
Final exam [data | solutions]
Introduction to Probability Theory [slides | notes | simulation]
Conditional Probability and Bayes' Rule [slides | notes]
Random Variables [slides | notes]
Probability Models in Econometrics [slides | notes]
Expectations [slides | notes]
Rubin Causal Model [slides | notes]
Limiting Distributions [slides | notes | code]
Point Estimation [slides | notes | code]
Maximum Likelihood Examples
Autoregressive Models [slides | notes]
Sampling Techniques [slides | notes]
Parametric Statistical Inference [slides | notes]
Bootstrapping [slides | notes]
Non-Nested Likelihood Ratio Test: A Bootstrapping Example [data]
Fisher's Exact Test and its Extensions [slides | notes]
Bayesian Analysis [slides | notes]
A Video Introduction to R courtesy of Google
Notes on R: A Programming Environment for Data Analysis and Graphics
R quick reference card
Econometrics in R
Google's R Style Guide
R Programming Wikibook
RStudio
Emacs and ESS
R in a Nutshell by Joseph Adler
R Inferno (see, especially, The Eighth Circle)
Stack Overflow's R Q&A board
ggplot2 Reference Manual
knitr (to link R and LaTeX)
Matlab/R Reference Guide
My teaching materials are licensed under a
Creative Commons Attribution-Noncommercial 3.0 United States License.