Omitted variable bias: a. exists if the omitted variable is correlated with the included regressor but is not a determinant of the dependent variable. b. exists if the omitted variable is correlated

Omitted variable bias:
a. exists if the omitted variable is correlated with the included regressor but is not a determinant of the dependent variable.
b. exists if the omitted variable is correlated with the included regressor and is a determinant of the dependent variable.
c. will always be present as long as the regression R2 < 1.
d. is always there but is negligible in almost all economic examples.

Expert Solution

Omitted variable bias exists if the omitted variable is correlated with the included regressor and is a determinant of the dependent variable.

In this case, there is no reason to use the independent variable when it could be replaced by one of its determinants. For example, if you have a variable that measures how often someone drinks coffee and another variable that measures how often they have insomnia, and you want to know whether they drink coffee in order to stay awake at night, then you should use the insomnia variable instead of coffee.

Omitted variable bias occurs when information about an omitted variable is incorporated into the analysis. This can happen in two ways:

1) The omitted variable may be correlated with the included regressor, and that correlation can be used to predict the dependent variable. This will cause a positive bias in your model. If you have a negative correlation between the two variables, this will also cause a positive bias in your model.

2) An omitted variable may be correlated with the included regressor but is not a determinant of the dependent variable. In this case, it will be treated as though it were one, and that could cause a negative bias in your model.

Option B is Correct.

Junaid answered 2 years ago

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