Question

Suppose you want to estimate the modelY =B0 +B1X1 +B2X2 +B3X3 +B4X4 . However, you cannot measure X4 ,so you estimateY =B0 +B1X1 +B2X2 +B3X3 instead.The results of your regression are subject to:

a. Autocorrelation.
b. Heteroscedasticity.
c. Multicollinearity.
d. Specification bias. Left out an important variable

Answer 1

Ans:  d. Specification bias.Left out an important variable.

Reason:Omitted variable bias occurs when a regression model leaves out relevant independent variables, which are known as confounding variables. This condition forces the model to attribute the effects of omitted variables to variables that are in the model, which biases the coefficient estimates.

This problem occurs because your linear regression model is specified incorrectly—either because the confounding variables are unknown or because the data do not exist. If this bias affects your model, it is a severe condition because you can’t trust your results.

For omitted variable bias to occur, the following two conditions must exist:

1. The omitted variable must correlate with the dependent variable.

2. The omitted variable must correlate with at least one independent variable that is in the regression model.