Ordinary/Conditional Least Squares vs. Maximum Likelihood

krez

New Member
#1
Can anyone explain in layman's terms when we should use maximum likelihood as opposed to ordinary or conditional least squares for regression or time series analysis?

I know that when the regressors are stochastic we should use ML because the unbiased assumption of OLS/CLS will be violated, but there has to be other reasons as well. Like what statistical properties of the regressors or data set would make you prefer ML to OLS/CLS?

Thanks.