Causality: Fixed Effects

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Thank you very much, form Kiribati islands

MrAMerang
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Always very helpful, huge fan if yours! Cheers from Barcelona

GuifreBallesteSantacana
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very interactive and engaging, thank you very much

alan
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Hi Nick, i'd like thank you for this serie of videos. They are both supportive and helpful to people like me - someone new in causality.

claudianoneto
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FYI - per my notes, there are two ways of removing fixed effects - one method he's talking about sounds like "demeaning" and another method is first differencing. Correct me if I'm wrong :)

dal-qigv
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Great video! Thank you for the explanation! :D I will check out the other videos on your channel!

Pooh
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You're trully helpful! Thanks a lot

marcosahertian
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Hi Nick. I have just started following your causality series, and it really is wonderful. I just wonder, in the case of fixed effect, does it could unintentionally control the collider and thus make a bias? let's say for the height vs basket ability in the NBA example (assuming there is height variation in each year, while there is no variation in NBA status across years)

donoiskandar
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Thanks a lot for the video. Let‘s suppose I have a cross-sectional dataset with each observation being a different firm. The firm’s can be from two different industries. Did I understand you correctly that it is impossible to make a firm fixed effects regression on this? And also no industry fixed effects regression as we only have two industries? Would appreciate a response from you a lot! Many thanks!

ramonkonig
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Is age considered something fixed about a person? I know age change over time, but it changes for all individuals in the same way. I can't figure out if I should control for age in my two-way fixed effects model?

LouiseLund
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Hi Nick

I am doing some regressions and have models where i include year FE and others where i inculde year FE and country FE. Adding country fixed effects removes all statistical significance from my models. Would it then be appropriate to say:

"Interestingly the inclusion of country fixed effects removes all significance from all the models. This means that by controlling for country specific unobservable in the model there is no longer any significant results. This could mean that there is a variable that explains how voting coincidence changes over time that is not included in our models. This could be a variable that isn’t included in our dataset or even a variable that is not able to be measured."

Thanks.

thatlad
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would you be willing to share the animation code?

dr.kingschultz