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Fixed Effects (The Effect, Videos on Causality, Ep 45)

Econometrics, Causality, and Coding with Dr. HK

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Please visit www.theeffectbook.net to read The Effect online for free, or find links to purchase a physical copy or ebook.
The Effect is a book about research design and causal inference. How can we use data to learn about the world? How can we answer questions about whether X causes Y even if we can't run a randomized experiment? The book covers these things and plenty more. These videos are meant to accompany the book, although they can also be viewed on their own.
This video relates to material found in Chapter 16 of the book.
A version of this video without background music can be found here: • Fixed Effects (The Eff...
What can we do if we can't measure all the things we need to control for? Well, if the setup is just right, we may be able to control for (some of) them anyway using fixed effects! Fixed effects is all about controlling for a set of unmeasured variables that are fixed within individual by... controlling for individual!

Опубликовано:

22 июн 2022

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Комментарии : 8

@RumbutterMcSquash 3 месяца назад

Excellent video. Those jump cuts are giving me epilepsy though.

@Damian-oo5jk 2 года назад

Dear professor Nick, thanks for your videos, they are very instructive. I am not sure if this is a place to ask you if you please could elaborate about the difference of the matching vs regression approach. I mean, to me is nearly the same. However, much of scholars claim causality when using matching techniques but only associations when using regression analysis (without dealing with IV). Would not be the same, if you control by the same amount of observables independent of using a matching or regression techniques? Why then one is associated with causality and the other with correlations? Thanks.

@NickHuntingtonKlein 2 года назад

In both cases you can only get a causal effect if you (match on)/(adjust for) the complete set of variables necessary to close all back doors. Outside of some corner cases to do with functional form, the set of necessary (matching variables)/(controls) is the same for both matching and regression, so anyone claiming causality from matching on a certain set of variables should also be willing to claim causality from controlling for that set of variables in regression. The reason you see people claiming causality in the matching context but not regression is more to do with sociology than statistics. Fields where matching is more popular, historically, been more willing to claim causality after controlling for a set of measured variables ("selection on observables") while fields where regression has been more popular have historically been more suspicious of "selection on observables" assumptions. This is true even when they're working in the same setting, so one of them is definitely wrong.

@Damian-oo5jk 2 года назад

@@NickHuntingtonKlein Thanks for your quick answer. Now I see that is something that has to do more with the research field. Maybe one difference could be with respect to the comparison group, which could affect the size of the treatment effect. Thanks again!

@Allu-oe6ih 2 месяца назад

Hi Nick! Thank you for a great video. I’m wondering if the fixed-effect approach works the same way with robust regression (rlm) and quantile regression (quantreg) in r as in case of OLS. Meaning that I’m adding a dummy for each id and/or time period.

@bisiadeyemo3082 Месяц назад

The video will be a lot better if you explain the coefficients instead glancing through it. Even your book, you barely explain the coefficients. It’s not just you, other books on advanced methods do not do a very good job of explaining the coefficients.

@NickHuntingtonKlein Месяц назад

What about the section titled "How do we interpret the results of this regression once we have estimated it?"

@bisiadeyemo3082 Месяц назад

@@NickHuntingtonKlein in journal articles, you are presented with only the coefficients and most students typically have problems explaining it. This is by far more important than the inner workings because most statistical software will do the calculations for you. I read through your instrumental variable section, and you barely explain the results of the first stage regressions. Similar with DID and regression discontinuity. This is not just you, several of the books that I have read, tend to pay little attention to the explaining coefficients