Question

Explain, theoretically, the consequences of omitting relevant variables in econometric modelling.

06 Jul 2022
Answer :
Word Count : 1138

when omitting a variable, it will show up in the residual, i.e. it will show up in the error term. Thus, the error term and independent variables are necessarily going to be correlated. This clearly violates the assumption that the error term and the independent variables must be uncorrelated.

The problem of the omitted variable bias is pretty serious. An omitted variable leads to biased and inconsistent coefficient estimate. And as we all know, biased and inconsistent estimates are not reliable. From our previous post, you might remember how omitting a variable can change the signs of the coefficients, depending on the correlation of the omitted variable with the independent and explanatory variables. Thus, coefficients also become unreliable. Hence, the regression model will fail completely. In a simple simulation exercise, I tried to visualize what happens if we neglect a relevant variable from a regression models. The exercise confirms that when neglecting a relevant variable from the model, OLS fails to estimate the coefficients correctly.

Omitted variable bias is a fundamental regression concept that frequently arises in antitrust litigation. Every regression has omitted some variable. The relevant question is whether the omission generates bias _________ ____ ______ __________ ______ ________ ___ ___ _____ ___ ___ _______.
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