One of the advantages of using Stata for linear regression is that it can automatically use heteroskedasticity-robust standard errors simply by adding , r to the end of any regression command. Anyone can more or less use robust standard errors and make more accurate inferences without even thinking about what they represent or how they are determined since it’s so easy just to add the letter r to any regression.
As I have mentioned in other posts, this semester I trying to maximize the number of intuitive visualizations for my students to master economic and mathematical concepts more in my teaching, as well as develop my own R skills. For my intermediate microeconomics course, I recently put together an interactive Shiny App with R that demonstrates the effect of a tax on consumers and producers.
Simply input your own (inverse) demand and supply functions and the size of the tax, and the graph and summary will update with the market equilibrium and the surpluses lost by the tax.
For my econometrics course this semester, I have been using R to help students visualize linear regression models. Running a regression in R is quite simple, as is intepretting the results, with a little bit of training. However, I emphasize that I want students to understand what is happening “inside the black box” of regression. I discourage blindly trusting R’s opaquely simple input and output, and get students to learn what R is doing under the hood, even if they will never have to manually estimate the model themselves.
This semester, I am posting my lecture slides for my Econometrics class on GitHub. While I usually post my lecture slides for my courses on my website or link to my Dropbox, those are the final PDF documents. On GitHub, I am posting both the final PDFs as well as the source .rmd files. I am primarily doing this for my econometrics students, who will be learning R and R Markdown for their assignments (which is how I write my slides), but this is also open to anyone.