Question

19.What may be the effects of underspecifying (omitted variable) a model.

Answer 1

Suppose that we omit a variable that actually belongs in the
true (or population) model.
This is often called the problem of excluding a relevant
variable or under-specifying the model.
This problem generally causes the OLS estimators to be
biased.The bias in this case arises from omitting the
explanatory variable.

The omitted variable goes into the error term in the regression equation. For omitted variable bias to exist, we know that one of the conditions is that it is correlated with at least one other explanatory variable. So, clearly, your error term and independent variables are not uncorrelated. Violation of this assumption of classical linear regression model causes the OLS estimator to be biased and inconsistent.

The problem of omitted variable bias is quite serious because if your estimates are biased and inconsistent, they are not reliable.

Omitting variables from our regression model can bias the coefficient estimates. When we are assessing the effects of the independent variables in the regression output, this bias can produce the following problems:

• Overestimate the strength of an effect.
• Underestimate the strength of an effect.
• Change the sign of an effect.
• Mask an effect that actually exists

We don’t want any of these problems to affect your regression results!