What are the consequences of including irrelevant variables in a model?
When an irrelevant variable is included, the regression does not affect the unbiasedness of the OLS estimators but increase their variances.
How do you identify omitted variables?
How to Detect Omitted Variable Bias and Identify Confounding Variables. You saw one method of detecting omitted variable bias in this post. If you include different combinations of independent variables in the model, and you see the coefficients changing, you’re watching omitted variable bias in action!
What is an irrelevant variable?
We define irrelevant variables as having only zero factor loadings, whereas relevant variables are related to at least one factor. Thus the irrelevant variables are not related to the factors and, vice versa, do not contain information for estimating the unobserved factors.
Which is better a BN or a causal model?
Instead of building a causal model with a large scope and a limited number of variables, it could be better to create a BN with a reduced scope (e.g. not portfolio management but project management) with an exhaustive and necessary number of variables.
Can a variable be excluded from a regression model?
Generally, variables should not be excluded from a model just because one does not like the sign of their esimated coefficients!
Are there any problems with reporting p-values for all variables?
One problem with reporting the p-values for all the variables (including the non-significant ones) is that the results may be misleading because of the correlation of the independent variables.
Which is independent variable ends up being significant?
Which variable ends up being significant may have to do with which one is a better predictor, or may have to do with which one was entered into the model first. One solution to the problem of having correlated independent variables is to report the simple correlation of the independent variable and the dependent variable.