Econometrics Blog
Even for experienced empirical researchers, certain econometric issues can be often overlooked or seem confusing. It can seem like everyone else knows what’s going on except you! The rapid pace with which new econometric methods are being developed further exacerbates these issues. Who has the time to keep up to date?
Enter, Professor Daniel Millimet from the SMU Department of Economics. His blog, “How the (Econometrics) Sausage is made” lays out what empirical practitioners need to know when it comes to those often overlooked or confusing issues as well as recently developed techniques in a simple, reader friendly way. His research spans microeconometric methods and applications in labor economics, environmental economics, and international trade and he teaches courses in labor economics and econometrics. He is also a research fellow at IZA, a senior co-editor of Advances in Econometrics, an associate editor of Empirical Economics, and a co-editor of the new Journal of the Association of Environmental and Resource Economists.
Chicken or Egg?
Fright Night
Why are significant results so highly coveted? A precisely estimated zero is informative and should not be described as the stuff of nightmares.
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Frame Job
What's the null hypothesis when testing an hypothesis surrounding issues related to racism? This is crucial because the answer entails a bias toward the null hypothesis.
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Where the Magic Happens
There are methods available for applied researchers to examine distributional effects of treatments. So, let's revisit a literature that has remained for far too long on the fringes of applied work.
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Skinning the Cat
In my econometrics classes, I preach to my students: "Do not ignore the endogeneity of covariates just because they are not of interest"
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Wait for It
Seeing the future and waiting for it aren't just common themes in the TV show Psych. They are also common themes -- either explicitly or implicitly -- in econometrics.
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Different, but the Same
To say that difference-in-differences (DID) has seen a resurgence in the past decade would be an understatement. That I'm am not much of a fan would also be a bit of an understatement.
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Don't Reinvent the Wheel
An often fruitful way to come up with new research questions, or new ways to answer existing research questions, is to borrow approaches that were designed for completely different purposes.
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Pick a Number
In case you were unaware, or I have neglected to mention it lately, measurement error exists and it is terrifying. Like the Yeti.
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The Average Man
It is well known to most, if not all, reading this that the coefficients in non-linear regression models are not of direct interest because they lack a meaningful interpretation. Instead, we focus on marginal effects.
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The Rollercoaster
With roller coasters, non-linearity leads to fright and sickness and thrill. In econometrics, non-linearity can lead to identification, which is thrilling, but it can also lead to fright and sickness if we don't fully appreciate what is happening.
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On Top of the World
I'm beyond sure that we are all sick (no pun intended) of seeing graphs about "flattening the curve. But, estimating turning points has a long history in econometrics and statistics.
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To Combine is Divine
Today I'm thinking about -- thanks to my lecture this morning -- fundamental mistakes: model mis-specification. As researchers, we all have had it ingrained into our psyche that the true model is never known.
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Up is Down
Rates of COVID-19 infection and mortality are impossible to identify right now without making heroic assumptions about selection into testing, availability of testing, share of asymptomatic cases, ... But what about partial identification?
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Tests for the rest of us!
Two simple specification tests are applicable a vast majority of empirical (micro) research, yet virtually never make an appearance. Like Rudolph, they get left out of all the fun games researchers play with data
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Shopping at the Gap
When data involve a time dimension, there is a gap that merits attention but most often is ignored: the gap between observed time periods in the data.
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The Non-Monotonicity of Wrongness
In addition to being perhaps the happiest statistician in the history of statistics, George said it best: "All models are wrong but some are useful".
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The Validity of External Validity
Usage of the terms "internal validity" and "external validity" in the causal inference literature has baffled me a bit. So, I did some investigating.
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Mostly Unidentified
A microeconometrics lesson from Miracle Max in one of the greatest movies ever made: "Turns out your friend here is only MOSTLY dead. See, mostly dead is still slightly alive."
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Time to Dance!
Surely there is something to to be learned from the time-series macroeconometrics literature that applied microeconometricians can utilize, no?
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Econometrics of Inflation
If you think this post is about inflation of the macro variety, you have come to the wrong place!
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Yet Another Post on LPM and Probit?
For those unaware, LPM is a fancy name for "I am going to use OLS even though the dependent variable is binary, but I want to feel special!"
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There is Exogeneity, and Then There is Strict Exogeneity
This week I handled a paper at one of the journals for which I am on the editorial board and was reminded of a mistake that I have seen all too frequently over the years...
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Endogeneity Measurement Error
As you can tell from my first few posts in this blog, I enjoy thinking about measurement error and endogeneity more generally!
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IV with a Mismeasured Binary Covariate
IV with Endogenous Controls
IV in Exactly Identified Models
You may remember (?) that the usual IV estimator is actually a biased, but consistent, estimator. But, do you remember that the expectation of the estimator does not even exist in exactly identified models!
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