University of California, Berkeley

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

startof 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.