Question

What are the other names that omitted variable bias is called?

Answer 1

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.

In the context of regression analysis, there are various synonyms for omitted variable bias. They are often refered to as confounding variables, confounders, and lurking variables. These are important variables that the statistical model does not include and, therefore, cannot control. Additionally, they call the bias itself omitted variable bias, spurious effects, and spurious relationships.

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