💡Metric Thread💡"OLS is complicated, mate! Proceed with caution!"

I love papers that look at OLS, a centuries-old tool, and still find something we did not know about it. They are surprising and very relevant for applied work. Here, I will list a few recent ones.

#EconTwitter @TymonSloczynski (doi.org/10.1162/rest_a…) shows that, when we have a binary treatment with heterogeneous effects, a simple OLS regression with controls captures a counterintuitive combination of ATUs and ATTs. @agoodmanbacon summarized Tymon's paper:

https://twitter.com/agoodmanbacon/status/1455262252441948164

💡Econometrics Thread💡Today, I will talk very briefly about a few recent methodological papers that I think are super useful to applied researchers.

Basically, below, you will find some new tools that may help you to answer relevant empirical questions. RDD: It is standard to implement placebo tests and to check whether the density of the running variable is continuous. But using standard tools for the placebo tests and McCrary's test for the density is inefficient.

💡Short Metrics Thread💡"Better Lee Bounds" by Semenova (2020, arxiv.org/pdf/2008.12720…).

Today, Semenova (sites.google.com/view/semenovav…) presented her work on the Chamberlain Seminar (chamberlainseminar.org/home). They will upload her presentation soon. First, I am a fan of partial identification and she has at least three papers on this topic. So I recommend visiting her page and checking all her papers. She also has work on Machine Learning.

💡Short Econometrics Thread💡Multiple Hypothesis Testing in Experimental Economics by List, Shaikh and Xu (2019, bit.ly/2XsXCWA)

Multiple Hypothesis Testing (MHT) is the kind of comment that econometricians love to make and applied researchers hate to hear. Asking someone to account for (MHT) usually means that the person will lose every star in their tables. Bonferroni's and Holm's (jstor.org/stable/4615733) adjusted p-value are simple to compute, but they may create tests that are very conservative.

💡Short Econometrics Thread💡Today, I will talk very briefly about a few recent papers expanding the synthetic control method (SCM). I will talk about three working papers: Abadie and L'Hour (2019), Grossi, Lattarulo, Mariani, Mattei and Oner (2020) and Cao and Dowd (2019). Abadie and L'Hour (2019, bit.ly/31aTaNV) handles a very important problem of the SCM. When there are more treated units than predictors, there is no unique solution to the minimization problem. Such a crowded situation arises when the treated unit is a city.

💡Econometrics Thread💡Marginal Treatment Effects (MTE)

Today, I will not talk about one particular paper. I will explain the basics of MTE and will mention the papers that I believe to be important. As my wife says, I like to preach and spread the Gospel of MTE. First, a disclaimer: I definitely missed some important and interesting papers in this very long thread. And I apologize if I forgot your favorite paper. In this case, feel free to include it in this thread!

💡Econometrics Tread💡Identification, Inference and Sensitivity Analysis for Causal Mediation Effects by Imai, Keele and Yamamoto (IKY, 2010, bit.ly/3emZ4Ag)

Mediation analysis decomposes a treatment effect in different causal mechanisms. It is a hard problem in social sciences and health sciences. IKY offer a simple nonparametric solution under a sequential exogeneity assumption. But before we discuss all the details, let's think about an example to fix ideas. (This is not their empirical application.)

💡Econometrics Thread💡”Cherry Picking with Synthetic Controls” by Ferman, Pinto and Possebom (2020, bit.ly/3bGKum7).

It was a long journey, but we have finally published our paper at @JPAM_DC. I’m really happy to see it on the journal’s website. I would like to thank the editor, Burt Barnow, and three anonymous referees whose comments helped us to improve our article.

💡Econometrics Thread: 💡Going beyond average effects with Diff-in-Diff. Recently, DiD methods became very popular and many excellent papers showed up! But, today, I want to talk about two papers that are not that recent, but are super interesting! @Susan_Athey and Imbens (2006, AI, onlinelibrary.wiley.com/doi/abs/10.111…) and Bonhomme and Sauder (2011, BS, jstor.org/stable/23015949) discuss how to extend the DiD framework to identify not only average effects, but also effects on other parts of the distribution.

Are you concerned with measurement error? Do you wanna know how partial identification can help you address this problem? This thread is for you! (@dlmillimet, I was inspired by your blog post!) 1/14 Many people think that binary variables are less likely to be mismeasured. But this is not the case! Education (bit.ly/2RqOBM7), Unionization (bit.ly/2OZPs4V) and Welfare Participation (bit.ly/34XNXJU) are all measured with error! 2/14