The Most Simple Explanation of the Endogeneity Bias and 2-Stage Least Squares Regression
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- เผยแพร่เมื่อ 19 ต.ค. 2024
- A simple, non-mathematical and intuitive explanation of the endogeneity bias and the importance of the 2-stage least squares (2SLS) model.
This is one of the fundamental models of econometrics and is able to untangle causality from spurious correlations. It is crucial for anyone using non-experimental data to understand the 2SLS model.
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Keywords: 2SLS, 2-stage least squares, 2 stage least squares, endogeneity bias, exogeneity, econometrics, statistics, OLS regression, regression analysis, GMM, maximum likelihood, two-stage least squares, instrumental variables, instruments, endogenous, exogenous, TSLS, economics, finance, stata, eviews, endogeniety
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the best explanation I've watched so far
Thank you so much for making this video! The best explanation I've ever found so far
Excellent explanation. Much better than my grad profs who made such concepts impenetrable.
It's a wonderful explanation. Thanks so much.
Thank you, you explained it very clearly! :)
Thank you for the video :-)
What are the differences between endogeneity and omitted variable bias? Can omitted variable bias be regarded as a type of endogeneity? Can you give me the examples as well?
thank you, this explanation was so helpful!
I really appreciate this vedio .. helpful and useful.
Thanks so much for this video!
thanks a lot!!! saviour
excellent explanation!!!!
Clear, thank you!
Thank you for the explanation :)
Thank you so much bro
thank you do much for this
Hi there I am seeing the effect of derivative usage on firm value, and so regression firm value with my independant variable derivative usage and set of control variables.
I know there is an endogeneity issue in the sense that there a characteristics both unobservable (eg managerial quality) and observable that have a postive effect on firm value and are postively correlated with derivative use.
I understand the enodogenity in this sense mean that these characteristics that are captured by the error term are linked to the the explantoryy variable deriavtive usage. What my main question is is which variables are ones reffered to as endogenous?
a)is it the firm value that is enodogenous or deriavtive usage that is enodogenous
b) or are firm value and derivative usage both "endogenous variables"
c) or is the observed/unobserved characterisitcs that are referred to as endogenous. Thanks so much.
Thank you!!!
I am not sure, but I think this illustrates "confounding" or "common causation." Endogeneity selection bias would result from a situation in which wages and education are independent (B1 = 0), but both cause something else, such as happiness. If we then regressed wages against BOTH education and happiness, it would result in a spurious negative relationship between education and wages (B1-hat < 0).
Maybe an issue of discipline (Elwert and Winship 2014. Annu. Rev. Sociol. 2014. 40:31-53
Very nice:)
So in theory, if you had a variable measuring competitiveness and it would correlate with both, your main explanatory variable (education) and you dependent variable (wages), then you would simply include it in your regression model? What about correlation with education?
Yes, if you can observe (and measure) competitiveness, you should include it in your model, since it's most likely correlated with both wage and education. Ignoring it would lead to omitted variable bias. If you don't have data for competitiveness, you can use an instrumental variable to avoid omitted variable bias. This instrument has to be both *relevant* (correlated with education) and *exogenous* (uncorrelated with competitiveness which is part of the error term). So in this example the instrument has to affect wage solely through education. 2SLS works as follows:
*1st stage regression:* Regress your endogenous variable (education) on ALL exogenous variables (including your instrument) to isolate the "good" part of education, that is uncorrelated with the error term (which includes the effect of competitiveness) and compute the fitted values of education. These fitted values are now exogenous, since they're just a linear combination of other exogenous variables.
*2nd stage regression:* Regress your dependant variable on the fitted values of education as well as the other exogenous variables (without the instrumental variable). That's basically your original model where you replaced education with the fitted values of education which you calculated in the first stage.
-> Be happy because you got rid of the nasty endogeneity in your model ;-) Your estimator is now unbiased (if there are no other issues).
can you do a video on heterogeneity?
I am in Indonesia and I need 2LS regression model 😭. Help
how to apply it in spss??
this is such click bait.... didn't even explain 2sls--just a commercial for your course
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Great video but total clickbait thumbnail. Gotta do what you gotta do I guess
clickbait
How is this the most simple explanation of 2SLS? You didn't explain it at all.
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what kinda accent is that?