The Ordinary Least Squares (OLS) method is used to estimate the parameters in a linear regression model. For the OLS estimator to be the Best Linear Unbiased Estimator (BLUE), it must satisfy the Gauss-Markov assumptions. These assumptions are: 1. Linearity : The relationship between the independent variables and the dependent variable is linear. 2. Random Sampling : The data is obtained through a random sample of the population. 3. No Perfect Multicollinearity : There is no perfect multicollinearity between the independent variables. 4. Zero Conditional Mean : The errors have an expectation of zero given any value of the independent variables. 5. Homoscedasticity : The errors have constant variance (σ2). 6. No Autocorrelation : The errors are uncorrelated with each other. Given these assumptions, the condition that is not necessary for OLS to be BLUE is: (a) All errors are normally distributed Normality of the errors is not required for the OLS estimator to be BLUE according to the Gauss-Markov theorem. Normality is only necessary if we want to make specific inference statements (like t-tests and F-tests) or for the errors to follow a normal distribution.
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