Question

18. Explain in your own words the reasons, process and limitations of the OLS estimator.

Answer 1

OLS-

ordinary least squares is a type of linear least squares method for estimating parameters(unknown) in a linear regression model. ... Under the additional assumption that the errors are normally distributed, ols is the mle(maximum likelihood estimator)

Ols -

are BLUE (. they are linear, unbiased and have the least variance among the class of all linear and unbiased estimators).. all this, one should not forget the Gauss-Markov Theorem holds only if the assumptions are satisfied...

Assumptions-

1.The regression model is linear in the coefficients and the error term

2.The error term has a population mean of zero..

3.All independent variables are uncorrelated with the error term..

4.Observations of the error term are uncorrelated with each other

5.The error term has a constant variance

6.No independent variable is a perfect linear function of other explanatory variables

7.The error term is normally distributed