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This post is not about Gladwellian pabulum. Rather, it is about the econometric problem posed by outliers, whose presence of extreme-valued observations in a data set whose presence might cause problems of estimation and inference, and which a few colleagues have asked for a ‘Metrics Monday post on a few weeks ago.

Outliers cause estimation problems because they bias point estimates. They cause inference problems because they cause standard errors to be too large, thereby making it more likely that one will fail to reject a false null, i.e., a type II error. For example, if you collect data on a random sample of the population, the bulk of the people in your data might be between 18 and 80 years old, but you might also have someone in there who is 110 years old–that person is an outlier. Or the bulk of your sample might be making between $30,000 and $300,000 a year, but you might also have someone in there who makes $200,000,000 a year–that person is also an outlier.

This post is part of a continuing series on The Books that Have Shaped My Thinking.

It’s the summer, so I have time to read, both for work and for pleasure, and I have time to read books instead of just journal articles and blog posts. This made me realize that while a lot of my thinking has been shaped by things that I have read in journal articles (economics is an article-based field) and in blog posts (there is no better means of spreading important ideas quickly), a large part of my thinking has been shaped by books, which often contain more exciting ideas than journal articles–because they face less strict of a review process, books can be more daring in their claims, and thus have more chances of causing you to change how you view the world.

So I decided to write this series of posts on books that shaped my thinking. I talked about development books, about food and agriculture books, about economic theory books, and about econometrics books so far; this week I will talk about writing-related books. This will most likely be the last installment in this series–this blog is about the economics of agriculture, food, and development, I doubt anyone wants to know about the books that have shaped my thinking when it comes to fiction, or philosophy, or other things.

My view of writing advice has changed over the years. When I launched this blog almost five years ago, I loved to read and talk about writing. After a while, I realized that if most of the people who talk constantly about writing spent more time writing instead of talking about writing, they would be much more productive, and so I made the conscious choice to write instead of talk about writing. There are few exceptions to this rule, and here they are.

Some recommendations are very general; others are eminently personal. I just hope you can find one or two that will also shape your own thinking. I’m sure I am forgetting a lot of important books I have read and which have also shaped my thinking, but I made this list by taking a quick look at the bookshelves in my office.

(We had family visiting over the weekend, so I didn’t have the requisite time to think up of a new topic for the ‘Metrics Monday series. I have a few ideas lined up for posts on outliers, dummy variables, and so on, but I would have needed more time to explore them. What follows is a rerun of a post that some of you might have missed the first time around in early 2014.)

On the Worthwhile Canadian Initiative blog, Frances Woolley had a good post about why beginner econometricians get so worked up about the wrong things:

[I]t is rare that I will have someone come to my office hours and ask “Have I chosen my sample appropriately?” Instead, year after year, students are obsessed about learning how to use probit or logit models, as if their computer would explode, or the god of econometrics would smite them down, if they were to try to explain a 0-1 dependent variable by running an ordinary least squares regression.

I try to explain: “Look, it doesn’t matter. It doesn’t make much difference to your results. It’s hard to come up with an intuitive interpretation of what logit and probit coefficients mean, and it’s a hassle to calculate the marginal effects. You can run logit or probit if you want, but run a linear probability model as well, so I can tell whether or not anything weird is going on with the regression.”

But they just don’t believe me.

Continue reading[Rerun] ‘Metrics Monday: Rookie Mistakes in Empirical Analysis