Which of the following can cause OLS estimators to be biased? Heteroskedasticity

Question

Which of the following can cause OLS estimators to be biased? Heteroskedasticity. Omitting an important variable. A sample correlation coefficient of .95 between two independent variables both included in the model. Suppose that average worker productivity at manufacturing firms (avgprod) depends on two factors, average hours of training (vagtrain) and average worker ability (avail): avg prod - beta_0 + beta_1 avgtrain + beta_2avgabil + u. Assume that this equation satisfies the Gauss-Markov assumptions. If grants have been given to firms whose workers have less than average ability, so that avgtrain and avgabil are negatively correlated, what is the likely bias in beta_1 obtained from the simple regression of avgprod on avgtrain?

Explanation / Answer

Solution:

3.7 Option 2 is correct

Explanation:

omitted-variable bias (OVB) occurs when a model is created which incorrectly leaves out one or more important causal factors. The "bias" is created when the model compensates for the missing factor by over- or underestimating the effect of one of the other factors.

3.8. By defintion , 2>0 and by assunption , Corr(x1,X2)<0. Therefore ,there is a negitive bias in 1^:E(1^)<1. this means that , on average accross different random samples , the simple regression estimator under estimates the effect of the training program. It is even possible that E(1^) is a negitive even though 1>0.

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