## PSC 505: Maximum Likelihood Estimation

This is my course page for PSC 505 (Maximum Likelihood Estimation). Here, I will post answers to problem sets, provide pertinent links, and so on.

## A quick introduction to computing for PSC 505 (with wishlist)

__Read the introduction__.

This document serves as an introduction to many of the key skills that will help the dedicated student in this course. While much of it is about R specifically, much of it pertains to more general things like workflow and programming.

## Problem set 1

__View the answer key__.

This problem set provided students with an introduction to monte carlo analysis. It includes annotating some existing code and running a monte carlo experiment focused on omitted variable bias.

## Problem set 2

__View the answer key__.

This problem set extended the monte carlo analysis of omitted variables to include logit models. As an added bonus, I provide explicit solutions for the omission of one variable in a two-variable DGP.

## Problem set 3

__View the answer key__.

This problem set provides the student with a chance to analyze real data from Lisa Martin's

*Coercive Cooperation*. It includes negative binomial regression in both standard and zero-truncated contexts.