# QUANT- please clear this up for me

Hope studying is going well for everyone…

I wanted to clarify some things from quant. I will put my conclusions below, and if anything is wrong id love for some of you quant gurus to chime in (because i absolutely suck at quant)

1. Heteroskedasticity- coefficients will still be consistent, but biased. standard errors will be biased and inconsistent

2. Serial correlation- coefficients will still be consistent, but biased. standard errors will be biased and inconsistent

3. multicollinearity- coefficients will be consistent, but biased. standard errors, again, will be biased and inconsistent

4. omitted variable- coefficients will be inconsistent and biased. This is the only one in which coefficient estimates are actually inconsistent.

I have no idea if whatever i just typed makes sense, so im hoping you guys can help me out by explaining what all this means a little more. Im running into way too many mock questions about this to not have a deeper understanding

Almost. To be more precise:

1. Heteroskedasticity- coefficients will still be consistent. Standard errors will likely be too small (but could be too big) and may causes type I errors (when you think something is significant when it should be not significant)

2. Serial correlation- coefficients will still be consistent. Standard errors will likely be too small, which causes type I errors (when you think something is significant when it should be not significant)

3. multicollinearity- coefficients will be consistent. Standard errors will likely be inflated and likely causes type II errors (when you think something is insignificant when it should be significant)

Coefficients are not unbiased when multicollinearity is present

Yes, they are, assuming multicollinearity is the only issue. Bias is a calculable quantity. Coefficient estimates being “unreliable” does not make them biased in a technical sense.