Assumptions:
i) The regression model is linear in the coefficients and the error term.
ii) The error term has a population mean of zero.
iii) All independent variables are uncorrelated with the error term.
iv) Observations of the error term are uncorrelated with each other.
v) The error term has a constant variance.
vi) No independent variable is a perfect linear function of other explanatory variables
vii) The error term is normally distributed.
If these assumptions hold true, the OLS leads to the best possible estimates. However, if the assumptions are violated, this results in bias in the parameter estimates. OLS is no longer the most efficient estimator and Standard errors may no longer remain unbiased.
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