Also the London Review of Books is better than the New Yorker send tweet.
1. we have Lauren Oyler, who do u have? Jia?? Sad. 2. we have a Nabokov bingo square so incendiary I'd get immediately permabanned if I tweeted a screenshot. 3. we have new hits from Anne Carson
I do love the New Yorker and 2 NYer articles - one about the Bostswanan diamond mines and one about the deep sea submarine - were 2 of the best things I read all year. but LRB is better and in your heart you know it.
my friend jessica just had a baby and i'm in like five twitter fights so we all have our own things going on
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Hello! Tamara Broderick, Ryan Giordano and I have a new working paper out!! It's called "An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?" arxiv.org/abs/2011.14999
Here comes the paper thread!!! Aaaaaah!!!
We propose a way to measure the dependence of research findings on the particular realisation of the sample. We find that several results from big papers in empirical micro can be overturned by dropping less than 1% of the data -- or even 1-10 points, even when samples are large.
It's friday, it's hot as heck, nobody can do any work, it's time to read @MWillJr's paper on the impact of repealing gun permit to purchase laws on gun prevalence, gun homicides and suicides: morganwilliamsjr.com/wp-content/upl…
Purely speaking as an applied econometrician, this paper has several things that I love and the first one is the use of generalized synthetic control. This is by now the most credible approach to understanding the impact that state-level legal changes like this are likely to have
Synthetic control is still generally underused by economists, who still seem to favour using a battery of fixed effects (perhaps not realising that there is a huge cost to stripping out variation). This paper is one of very few that I've seen that avoids that common trap.
This is a really great thread for what to do if your attention span and reading comprehension and mental processing is shot to hell -- like, actually shot to hell.
I have found this general methodology to be really useful in life. Basically, if you're in location X with something (say, how much work you're able to complete in a day), you're just making it worse for yourself if you say "I'm going to get it together and do 5X tomorrow!"
Just assume in general that at best, at VERY MOST, you can expect tomorrow to be today + a 5% improvement in some direction. If you can figure out what's the right direction, then you can start aiming for your 5% improvement. Then you build on that if you keep doing it.
Overfitting is probably the most important concept that is missing from mainstream econometrics classes. Caltech prof Yaser Abu-Mostafa has an incredible intro lecture to overfitting here from his ML course:
If you take metrics with me, of course, you learn about overfitting. But often, this lesson is painful: it contradicts the powerful instinct that one should prefer complex, ideally nonparametric models to simple models that place strong structure on the data.
Overfitting affects all inference problems in economics because it arises when the true data-generating process is too complex for you to feasibly capture given the data and the tools that you have, and this is always the case in social science.
Some folks outside Econ, especially in medicine, can't understand why the randomistas won a Nobel. I think this confusion arises because many people mistakenly believe the key ingredient to scientific progress is intelligence. But it isn't: the key ingredient is courage.
It's easy to see that doing randomized experiments could revolutionize our understanding of economic and social policy. But nobody was doing it, so nobody really knew. It was expensive, hugely time-consuming, and extremely risky.
Who had the guts to actually try it? Banerjee, Duflo and Kremer. And what's more they had the tenacity and foresight to pursue a new path and build the social and professional institutions that would allow others to join them if they chose.
Have you just accepted an offer of admission to an economics phd? Congratulations! Now read this excellent advice from Matthew Pearson on how to survive first year, it helped me and many of my friends: law.vanderbilt.edu/phd/How_to_Sur…
It also has the best cold open I've ever read: "Dear First-year Graduate Student,
Welcome to the threshold of hell (just kidding, more like the patio, or courtyard of hell)."
The one thing I disagree with here is I don't think you should want the phd - or anything - more than life itself. The most important thing is your physical and mental health, and you will have to be strong in holding to this priority in first year.