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. 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. 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 |

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