Lectures PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser. Topic 1 Introduction to the Course and to R Supplementary
material: R code and data for the GDP example. R code and data from the fertility example. Topic 2 Review of Matrix Algebra for Regression Supplementary
material: We will work through Kevin Quinn’s matrix algebra review (pdf). For a general review of the basic math we’ll need in the course, you can find the lecture notes for the CSSS Math Camp here. Topic 3 Linear Regression in Matrix Form and Supplementary material: You may find useful three review lectures on basic probability theory, discrete distributions, and continuous distributions. Topic 4 Inference and Interpretation of Linear Regression Supplementary material: Example code for estimating a linear regression, extracting confidence intervals for the parameters, and plotting fitted values with a confidence envelope. Topic 5 Specification and Fitting in Linear Regression Topic 6 Outliers and Robust Regression Techniques Topic 7 Time Series: Stochastic Processes Topic 8 Models of Stationary and Non-Stationary Time Series Supplementary material: Example code and csv data for estimating and interpreting ARMA models in R, and example code for estimating and interpreting ARIMA and ECM models. You will also need this helper function for plotting counterfactual time series using R base graphics. Topic 9 Introduction to Panel Data Analysis Supplementary material: Example code and csv data for estimating and interpreting panel ARIMA models in R. For the curious, the R script used to construct the example plots in the first half of this lecture is here. Student Assignments Problem Set 1 Due Tuesday, 15 April, in class Supplementary material: Data for problem 1 in comma-separated variable format. Problem Set 2 Due Friday, 25 April, in section Supplementary material: Data for problem 1 in comma-separated variable format. Problem Set 3 Due Tuesday, 6 May, in class Supplementary material: Five R script templates for simulation of the performance of linear regression with different kinds of data: when the Gauss-Markov assumptions apply; when there is an omitted variable; when there is selection on the response variable; when there is heteroskedasticity; and when there is autocorrelation in the response variable. Problem Set 4 Due Tuesday, 20 May, in class Supplementary material: Data for problem 2 in comma-separated variable format. Problem Set 5 (Optional) Due Friday, 6 June, in section Supplementary material: Data for problems 1. Data for problem 2. Data for problem 3. (All data in comma-separated variable format.) Final Paper Due Monday, 9 June, at 3:00 PM, in my Gowen mailbox See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers. Labs Lab 1 Introduction to R Lab 1 code and a page of links to R and LaTeX Resources. Lab 2 Beginning with data: reading in, exploring, and even regressing Lab 2 code and data for Example 1 and Example 2. Lab 3 More practice with regression and plotting Lab 3 code. Data is the same rossoil data as Example 1 in Lab 2. Lab 4 Useful things, largely loops and simulation Lab 4 code. Data for the first set of examples. Data for the second (model simulation) example. Lab 5 More on models and interpretation, simulation in detail Lab 5 code. Data for the first set of examples. It's the same rossoil data we've been using for weeks. Lab 6 Miscellaneous, transformation, and heteroskedasticity Lab 6 code. This will use two datasets: the same rossoil data we've been using and the democracy data from earlier. Lab 6+ Intro to LaTeX workshop Here is the tex file and pdf. Please also download the duck. Lab 7 Intro to logit and functions (and a bit of outliers) Lab 7 code. This will use two datasets: the same rossoil data we've been using and NES data for logit examples. Lab 8 Intro to simcf/tile (including for logit) Here is Lab 8 code. This lab will use the same datasets as last week's lab: the rossoil and NES data. We will also access the data for HW4 online in the script. Lab 8 Very brief intro to useful functions like apply() and plyr package Here is Lab 9 code. All data will be called within script. |

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