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Review of Linear Regression / Intro to Maximum Likelihood
Supplementary material: You may want to read through Kevin Quinn's matrix algebra review in pdf format. The R code to simulate heteroskedastic data and model that data using a heteroskedastic normal maximum likelihood is here. This is an advanced example; beginning R users should work through the code examples for Lab 1 and Lab 2 first.
Introduction to R
Supplementary material: R code and data for exploratory data analysis using histograms and boxplots. R code and data for a simple bivariate linear regression. R code and data for a multiple regression example. Interested students can find detailed instructions for downloading, installing, and learning my recommended software for quantitative social science here. Focus on steps 1.1 and 1.3 for now, and then, optionally, step 1.2.
Data Simulation, Model Fitting, and Interpretation of Results for Maximum Likelihood
Linear Regression in R
Supplementary material: R code for computing and plotting confidence intervals for a fitted regression line.
Time Series: Stochastic Processes
Models of Stationary Time Series
Models of Non-stationary Time Series
Basic Concepts for Panel Data
Supplementary material: For the curious, the R script used to construct the example plots in the first half of this lecture is here.
Variable Intercept Models for Panel Data
Supplementary material: Panel ARIMA and fixed effects models in R: example code and csv data for estimating and interpreting panel models with a large number of periods and a small number of cross-sectional units.
Panel and Time Series: Lingering Issues
Supplementary material: Arellano-Bond/Blundell-Bond models in R: example code, a helper function, and csv data for estimating and interpreting panel models with a small number of periods and a large number of cross-sectional units.
Advanced Topic 1
Panel and Time Series: Binary Dependent Variables
Advanced Topic 2
Multilevel Models: Maximum Likelihood and Bayesian Approaches
Advanced Topic 3
Missing Data and Multiple Imputation
Course Problem Set
Problems may be turned in at any time during the course