For the estimated coefficients of an econometric model to be most accurate and reliable, there are few properties we need to look for.The following describes the same.
Unbiasedness : An estimator is said to be an unbiased estimator if its mean or expected value is equal to the value of the true population parameter. More specifically, it means that if repeated samples of a given population are drawn then the average of all the parameter estimates ( estimated beta) would be equal to the actual population parameter. So, basically, unbiased estimators reflect accuracy of the estimation. The difference between the expected value of the estimator and the exact population parameter is known as the bias.
Minimum variance : An estimator is said to be a minimum variance or best estimator if its variance is less than the variance of any other estimator.
Efficiency : An estimator is efficient if the following 2 conditions are satisfied together
- the estimator is unbiased
- the estimator has the minimum variance
Linearity : An estimator is said to be linear if it is possible to express it as a linear combination of sample observations.
Consistency : If the increase in sample size (n) reduces bias (if there was bias, at all) and variance of the estimate and this continues until both bias and variance become zero as n approaches ∞, then the estimator is said to be consistent.
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