explain OLS assumptions and when they are biased and unbiased.

Expert Solution

A1. The linear regression model is “linear in parameters.”

A2. There is a random sampling of observations.

A3. The conditional mean should be zero.

A4. There is no multi-collinearity (or perfect collinearity).

A5. Spherical errors: There is homoscedasticity and no auto-correlation

A6: Optional Assumption: Error terms should be normally distributed.

If you look at the regression equation, you will find an error term associated with the regression equation that is estimated. This makes the dependent variable also random. If an estimator uses the dependent variable, then that estimator would also be a random number. Therefore, before describing what unbiasedness is, it is important to mention that unbiasedness property is a property of the estimator and not of any sample.

Unbiasedness is one of the most desirable properties of any estimator. The estimator should ideally be an unbiased estimator of true parameter/population values.

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jeff jeffy answered 1 year ago

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