Omitted Variable Bias

hassan.mahfooz · March 19, 2015, 9:44am

Can someone explain to me in English what this really means intuitively?

If omitted variable X2 is correlated with the remaining variable Xl

then a. error term in the model will be correlated with Xl b. the estimated values of regression coefficients will be biased and inconsistent c. the estimates of standard errors of regression coefficients and the coefficients themselves will both be inconsistent

darshanP · March 19, 2015, 3:59pm

Answer - a

Omission means variable that should have been included and it reduces accuracy as it has a correlation with other variables and error term, so this Omission makes T test biased and inaccurate and cascading effect on the hypothesis test

tickersu · March 20, 2015, 2:47am

It’s all three of those statements, not just A.

tickersu · December 8, 2015, 1:11am

hassan.mahfooz:

Can someone explain to me in English what this really means intuitively?

If omitted variable X2 is correlated with the remaining variable Xl

then a. error term in the model will be correlated with Xl The error term captures anything that we do not account for in our model to predict the DV. If X1 and X2 are correlated, but we have left X2 out of our regression, X2 will be accounted for in the error term. Thus, X1 is correlated with the error term (this also assumes that Beta2 is nonzero). b. the estimated values of regression coefficients will be biased and inconsistent Because the omitted variable is correlated with one or more of the other independent variables (and that omitted Beta is nonzero), a bias is introduced into the calculated coefficients. Additionally, the consistency property is lost (sample size increase no longer shrinks the standard error for the estimator). c. the estimates of standard errors of regression coefficients and the coefficients themselves will both be inconsistent Because the error term accounts for a variable that we need in the regression, the error term is not properly estimated. Therefore, the error variance is not correctly estimated. The standard error of each coefficient is a function of the error variance, so the standard errors are improperly estimated as well. Check out this link if you’d like to see a little more math behind the explanations or want a further explanation. https://files.nyu.edu/mrg217/public/specification_handouts.pdf

darshanP · March 20, 2015, 7:06am

I agree…my bad…I only looked at the first option…where as my explanation supports all the three answer…Sorry need to change my habit

and indeed way better explanation…cheers

Thanks

hassan.mahfooz · March 21, 2015, 2:46pm

Thanks a lot for your replies !

tickersu · March 21, 2015, 8:30pm

No problem, hope it helped!