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Archive of posts filed under the Economics category.

“Unbiasedness”: You keep using that word. I do not think it means what you think it means. [My talk tomorrow in the Princeton economics department]

The talk is tomorrow, Tues 24 Feb, 2:40-4:00pm in 200 Fisher Hall: “Unbiasedness”: You keep using that word. I do not think it means what you think it means. Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University Minimizing bias is the traditional first goal of econometrics. In many cases, though, the […]

Oh, it’s so frustrating when you’re trying to help someone out, and then you realize you’re dealing with a snake.

This happens sometimes. Someone comes to you with a request, maybe it’s a student or a potential student or just someone who has a question relating to your field of expertise. You’re in a good mood so you decide to help out, or maybe you feel it’s your duty to be helpful, or, who knows, […]

Economics/sociology phrase book

Mark Palko points me to this amusing document from Jeffrey Smith and Kermit Daniel, translating sociology jargon into economics and vice-versa. Lots of good jokes there. Along these lines, I’ve always been bothered by economists’ phrase “willingness to pay” which, in practice, often means “ability to pay.” And, of course, “earnings” which means “how much […]

Cognitive vs. behavioral in psychology, economics, and political science

I’ve been coming across these issues from several different directions lately, and I wanted to get the basic idea down without killing myself in the writing of it. So consider this a sketchy first draft. The starting point is “behavioral economics,” also known as the “heuristics and biases” subfield of cognitive psychology. It’s associated with […]

What’s misleading about the phrase, “Statistical significance is not the same as practical significance”

You’ve heard it a million times, the idea is that if you have an estimate of .003 (on some reasonable scale in which 1 is a meaningful effect size) and a standard error of .001 then, yes, the estimate is statistically significant but it’s not practically significant. And, indeed, sometimes this sort of thing comes […]

Relaxed plagiarism standards as a way to keep the tuition dollars flowing from foreign students

Interesting comment thread at Basbøll’s blog regarding the difficult position of college writing instructors when confronted with blatant student plagiarism. Randall Westgren writes: I believe the easiest part of the patchwriting [plagiarism] phenomenon to understand is why writing instructors are leading the charge. Professor Howard is caught between a herd of high-value (i.e. full-tuition and […]

It’s Too Hard to Publish Criticisms and Obtain Data for Replication

Peter Swan writes: The problem you allude to in the above reference and in your other papers on ethics is a broad and serious one. I and my students have attempted to replicate a number of top articles in the major finance journals. Either they cannot be replicated due to missing data or what might […]

Message to Booleans: It’s an additive world, we just live in it

Boolean models (“it’s either A or (B and C)”) seem to be the natural way that we think, but additive models (“10 points if you have A, 3 points if you have B, 2 points if you have C”) seem to describe reality better—at least, the aspects of reality that I study in my research. […]

“Now the company appears to have screwed up badly, and they’ve done it in pretty much exactly the way you would expect a company to screw up when it doesn’t drill down into the data.”

Palko tells a good story: One of the accepted truths of the Netflix narrative is that CEO Reed Hastings is obsessed with data and everything the company does is data driven . . . Of course, all 21st century corporations are relatively data-driven. The fact that Netflix has large data sets on customer behavior does […]

Subtleties with measurement-error models for the evaluation of wacky claims

Paul Pudaite writes: In the latest Journal of the American Statistical Association (September 2014, Vol. 109 No. 507), Andrew Harvey and Alessandra Luati published a paper [preprint here] — “Filtering With Heavy Tails” — featuring the phenomenon you had asked about (“…(non-Gaussian) models for which, as y gets larger, E(x|y) can actually go back toward […]