Omitted Variable Bias with Many Regressors (2 pages)

Previewing page 1 of actual document. View the full content.
View Full Document

Omitted Variable Bias with Many Regressors



Previewing page 1 of actual document.

View the full content.
View Full Document
View Full Document

Omitted Variable Bias with Many Regressors

830 views

Lecture 14


Lecture number:
14
Pages:
2
Type:
Lecture Note
School:
Cornell University
Course:
Econ 3120 - Applied Econometrics
Edition:
1

Unformatted text preview:

Econ 3120 1st Edition Lecture 14 Outline of Current Lecture I Unbiasedness of Multivariate OLS Estimators Current Lecture I Omitted Variable Bias with Many Regressors 6 3 Omitted Variable Bias with Many Regressors The above discussion only applies to a model with two independent variables When there are more than two independent variables and we leave one out things get much more complicated If this happens all of the estimated 0 s can be biased and the bias of j in the short regression generally depends on the relationship between the omitted variable and all the x s and on the relationship between all the x s But generally speaking if we assume that x j is uncorrelated with the other included x s then we can say something about the bias of the estimated coefficient Suppose the true model is log wage 0 1educ 2exper 3abil u where exper is years of experience and we leave out abil If we assume that education and experience are uncorrelated then the expectation of 1 in the regression log wage 0 1educ 2exper u will be E 1 1 3 Cov educ d abil Vard educ which is essentially the same thing as the formula 7 above Therefore we might expect 1 to be biased upwards if 3 is positive and education and ability are positively correlated Economists often try to sign the bias using this formula regardless of whether x j the variable of interest in the short regression is uncorrelated with the other included x 0 s This is a reasonable shortcut but it s important to know that it isn t exactly right especially if the included x s are highly correlated 2 2The general form for the estimate of j in the short regression when xk is omitted is j j k j where j is the coefficient on xj in the regression of xk on all of the included regressors See Wooldridge Section 3A for more detail 9 7 Variance of OLS Estimators To obtain the variance of OLS estimators we need make an assumption analogous to SLR 5 MLR 5 Homoskedasticity The error term in the OLS equation described by MLR 1 has constant



View Full Document

Access the best Study Guides, Lecture Notes and Practice Exams

Loading Unlocking...
Login

Join to view Omitted Variable Bias with Many Regressors and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Omitted Variable Bias with Many Regressors and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?