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Archive of posts filed under the Economics category.

I hate that “Iron Law” thing

Dahyeon Jeong wrote: While I was reading your today’s post “Some people are so easy to contact and some people aren’t”, I’ve come across your older posts including “Edlin’s rule for routinely scaling down published estimates.” In this post you write: Also, yeah, that Iron Law thing sounds horribly misleading. I’d not heard that particular […]

Fitting multilevel models when predictors and group effects correlate

Ryan Bain writes: I came across your ‘Fitting Multilevel Models When Predictors and Group Effects Correlate‘ paper that you co-authored with Dr. Bafumi and read it with great interest. I am a current postgraduate student at the University of Glasgow writing a dissertation examining explanations of Euroscepticism at the individual and country level since the […]

“A mixed economy is not an economic abomination or even a regrettably unavoidable political necessity but a natural absorbing state,” and other notes on “Whither Science?” by Danko Antolovic

So. I got this email one day, promoting a book that came with the following blurb: Whither Science?, by Danko Antolovic, is a series of essays that explore some of the questions facing modern science. A short read at only 41 pages, Whither Science? looks into the fundamental questions about the purposes, practices and future […]

Using Stan to improve rice yields

Matt Espe writes: Here is a new paper citing Stan and the rstanarm package. Yield gap analysis of US rice production systems shows opportunities for improvement. Matthew B. Espe, Kenneth G. Cassman, Haishun Yang, Nicolas Guilpart, Patricio Grassini, Justin Van Wart, Merle Anders, Donn Beighley, Dustin Harrell, Steve Linscombe, Kent McKenzie, Randall Mutters, Lloyd T. […]


Kevin Lewis points to a research article by Lawton Swan, John Chambers, Martin Heesacker, and Sondre Nero, “How should we measure Americans’ perceptions of socio-economic mobility,” which reports effects of question wording on surveys on an important topic in economics. They replicated two studies: Each (independent) research team had prompted similar groups of respondents to […]

Mick Cooney: case study on modeling loss curves in insurance with RStan

This is great. Thanks, Mick! All the Stan case studies are here.

Should we worry about rigged priors? A long discussion.

Today’s discussion starts with Stuart Buck, who came across a post by John Cook linking to my post, “Bayesian statistics: What’s it all about?”. Cook wrote about the benefit of prior distributions in making assumptions explicit. Buck shared Cook’s post with Jon Baron, who wrote: My concern is that if researchers are systematically too optimistic […]

“5 minutes? Really?”

Bob writes: Daniel says this issue is an easy 5-minute fix. In my ongoing role as wet blanket, let’s be realistic. It’s sort of like saying it’s an hour from here to Detroit because that’s how long the plane’s in the air. Nothing is a 5 minute fix (door to door) for Stan and […]

“From ‘What If?’ To ‘What Next?’ : Causal Inference and Machine Learning for Intelligent Decision Making”

Panos Toulis writes in to announce this conference: NIPS 2017 Workshop on Causal Inference and Machine Learning (WhatIF2017) “From ‘What If?’ To ‘What Next?’ : Causal Inference and Machine Learning for Intelligent Decision Making” — December 8th 2017, Long Beach, USA. Submission deadline for abstracts and papers: October 31, 2017 Acceptance decisions: November 7, 2017 […]

Apply for the Earth Institute Postdoc at Columbia and work with us!

The Earth Institute at Columbia brings in several postdocs each year—it’s a two-year gig—and some of them have been statisticians (recently, Kenny Shirley, Leontine Alkema, Shira Mitchell, and Milad Kharratzadeh). We’re particularly interested in statisticians who have research interests in development and public health. It’s fine—not just fine, but ideal—if you are interested in statistical […]

Causal inference using data from a non-representative sample

Dan Gibbons writes: I have been looking at using synthetic control estimates for estimating the effects of healthcare policies, particularly because for say county-level data the nontreated comparison units one would use in say a difference-in-differences estimator or quantile DID estimator (if one didn’t want to use the mean) are not especially clear. However, given […]

Looking for the bottom line

I recommend this discussion of how to summarize posterior distributions. I don’t recommend summarizing by the posterior probability that the new treatment is better than the old treatment, as that is not a bottom-line statement!

Rosenbaum (1999): Choice as an Alternative to Control in Observational Studies

Winston Lin wrote in a blog comment earlier this year: Paul Rosenbaum’s 1999 paper “Choice as an Alternative to Control in Observational Studies” is really thoughtful and well-written. The comments and rejoinder include an interesting exchange between Manski and Rosenbaum on external validity and the role of theories. And here it is. Rosenbaum begins: In […]

Causal identification + observational study + multilevel model

Sam Portnow writes: I am attempting to model the impact of tax benefits on children’s school readiness skills. Obviously, benefits themselves are biased, so I am trying to use the doubling of the maximum allowable additional child tax credit in 2003 to get an unbiased estimate of benefits. I was initially planning to attack this […]

“Mainstream medicine has its own share of unnecessary and unhelpful treatments”

I have a story and then a question. The story Susan Perry (link sent by Paul Alper) writes: Earlier this week, I [Perry] highlighted two articles that exposed the dubious history, medical ineffectiveness and potential health dangers of popular alternative “therapies.” Well, the same can be said of many mainstream conventional medical practices, as investigative […]

He wants some readings on the replication crisis that are accessible to college freshmen in economics

Harvey Rosen writes: My query is similar to the one from André Ariew that you posted on August 7, in which he asked if you could suggest readings for his graduate course in philosophy. I occasionally teach an undergraduate course on introductory microeconomics. I like to devote some time to discussing challenges to economists’ conventional […]

Also holding back progress are those who make mistakes and then label correct arguments as “nonsensical.”

Here’s James Heckman in 2013: Also holding back progress are those who claim that Perry and ABC are experiments with samples too small to accurately predict widespread impact and return on investment. This is a nonsensical argument. Their relatively small sample sizes actually speak for — not against — the strength of their findings. Dramatic […]

The Pandora Principle in statistics — and its malign converse, the ostrich

The Pandora Principle is that once you’ve considered a possible interaction or bias or confounder, you can’t un-think it. The malign converse is when people realize this and then design their studies to avoid putting themselves in a position where they have to consider some potentially important factor. For example, suppose you’re considering some policy […]

Delegate at Large

Asher Meir points to this delightful garden of forking paths, which begins: • Politicians on the right look more beautiful in Europe, the U.S. and Australia. • As beautiful people earn more, they are more likely to oppose redistribution. • Voters use beauty as a cue for conservatism in low-information elections. • Politicians on the […]

Died in the Wool

Garrett M. writes: I’m an analyst at an investment management firm. I read your blog daily to improve my understanding of statistics, as it’s central to the work I do. I had two (hopefully straightforward) questions related to time series analysis that I was hoping I could get your thoughts on: First, much of the […]