CORNELL ECON 3120 - Omitted Variable Bias with Many Regressors (2 pages)
Previewing page 1 of 2 page document View the full content.Omitted Variable Bias with Many Regressors
Previewing page 1 of actual document.
View the full content.View Full Document
Omitted Variable Bias with Many Regressors
0
0
920 views
Lecture 14
- Lecture number:
- 14
- Pages:
- 2
- Type:
- Lecture Note
- School:
- Cornell University
- Course:
- Econ 3120 - Applied Econometrics
- Edition:
- 1
Documents in this Packet
-
Lecture 28: Estimation of the Multiple Regression Model
1 pages
-
Lecture 27: Instrumental Variables
1 pages
-
Lecture 26: Counterfactual in Policies
2 pages
-
Lecture 25: Difference in Differences
1 pages
-
Lecture 24: Counterfactual Averages
2 pages
-
1 pages
-
Lecture 22: Dependent Variable Errors
1 pages
-
Lecture 21: Specification and Data Issues
1 pages
-
2 pages
-
2 pages
-
2 pages
-
Lecture 18: Generalized Least Squares and Feasible Generalized Least Squares
2 pages
-
2 pages
-
Lecture 16: The Linear Probability Model
1 pages
-
Lecture 15: Omitted Variable Bias with Many Regressors
2 pages
-
3 pages
-
Lecture 13: Unbiasedness of Multivariate OLS Estimators
2 pages
-
2 pages
-
Lecture 11: I. Goodness of Fit II. Unbiasedness
2 pages
-
Lecture 9: One Sided Tests, Confidence Intervals
2 pages
-
2 pages
-
6 pages
-
Lecture 7: One Sided Tests, Confidence Intervals
2 pages
-
2 pages
-
Lecture 5: Asymptotic Normality
2 pages
-
2 pages
-
2 pages
-
Lecture 2: Marginal Distributions
2 pages
-
2 pages
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
ECON 3120: Exam 2
Unlocking...