This Fall semester, I have made dedicated websites for all of my courses at Hood College that host nearly all the course content. You can see them all here. My interest was sparked when I saw Andrew Heiss’ amazing course websites.
Until this point, all of my course content has lived on Blackboard for my students, though I have also tried to post syllabi and lecture slides (if not additional resources) on my personal website over the past few years.
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This summer, I am overhauling my econometrics class in many ways, in part because I was pleased to recieve a teaching grant from my college to make more R resources for my econometrics class. Last Fall was the first time I had taught it using R, and I’ve learned a ton since then. Expect a flurry of posts in the coming weeks more on those topics.
This post, however, explores some of the trends that I have been thinking about in teaching econometrics, and something monotonous that I have been struggling with that encapsulates the tension in these trends: what to name my course.
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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.
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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.
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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.
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