Lectures PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser. Topic 1 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 Lab 1 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. Topic 2 Data Simulation, Model Fitting, and Interpretation of Results for Maximum Likelihood Lab 2 Linear Regression in R Supplementary material: R code for computing and plotting confidence intervals for a fitted regression line. Topic 3 Time Series: Stochastic Processes Supplementary material: R code showing simulation of stationary processes, and code for simulation of non-stationary processes. Topic 4 Models of Stationary Time Series Supplementary material: Example code and csv data for estimating and interpreting ARMA models in R. Topic 5 Models of Non-stationary Time Series Supplementary material: Example code for estimating and interpreting ARIMA and ECM models in R. Topic 6 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. Topic 7 Variable Intercept Models for Panel Data Supplementary material: Example code and csv data for estimating and interpreting panel ARIMA models in R. Topic 8 Panel and Time Series: Lingering Issues Supplementary material: Example R code for the Arellano-Bond method using simulated data can be found here. 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 Student Assignments Course Problem Set Problems may be turned in at any time during the course Supplementary material: Data for problems 1, 3, and 4 in comma-separated variable format. R code for problem 4. Data for problems 5. Data for problem 6. |

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