Variance and Standard Error of OLS Estimates | Introductory Econometrics 11

Hi, I am Bob. In the last video, we learned that the OLS estimates are unbiased under the zero conditional mean assumption. The unbiasedness of the OLS intercept and slope estimators means if we randomly sample data from the population many times and run the OLS regressions, the average of those estimates is centered at the population parameter. It is in the sense of sampling distribution when we have many samples from the same population. However, in our research, we usually have only one sample. So the OLS estimates of beta zero and beta one may be very close to the population value or far away from the value. The shape of the distribution of the OLS estimators, beta hat zero and beta hat one, can help us understand how far the estimates are from the true value. The variance of the OLS estimators is a measure of spread in the distribution of the OLS estimators. It measures how far the estimates are from the population value. For an unbiased estimator, the smaller the variance, the better the estimator.
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#Variance #Homoskedasticity #StandardError #StandardDeviation #OLSEstimator #Slope #Intercept #IntroductoryEconometrics

[@lessthanzero.]

I just want to say that your thoroughness on the solutions and actually showing the steps in the solution is really useful. Thanks for this!

[@MasumVatandas]

You are a great professor! Showing it on STATA made me better understand the concept. Thank you ❤

[@imhometree620]

Thank you so much! Very helpful video.

[@DhanviPatel-rq7je]

Thank you for these helpful videos! Will there be more for the rest of the chapters?

[@eraadahmed897]

sir, can you show the standard ERROR OF THE BETA NOT. i cannot solve it