ECOM30001 Lecture Notes - Lecture 4: Simple Linear Regression, Statistical Inference, Tachykinin Receptor 1

• School: University of Melbourne
• Department: Econometrics
• Course: ECOM30001
• Professor: Andrew Clark

Document Summary

Two ways of understanding the theorem: unbiased estimators of ( s). Use ols residuals, where the mlrm residuals are given by: Estimator of the error variance: variance of ols estimators depends on (cid:2870) is a function of this, require an estimate of (cid:2870) to calculate variance-covariance matrix for ols estimators. (cid:2779)= (cid:4666) (cid:2778)(cid:4667: numerator = sum of squared residuals, denominator = degrees of freedom where k+1 is the number of parameters being estimated. Subtract number of parameters from the sample size (n) T h e e s t i m a t e d e r r o r v a r i a n c e: residual std. Error gives the standard deviation : need to raise this to the power of 2 to get the variance (cid:2870) The variation in the dependent variable y about its mean that is explained by the regression model: high r2 means a high correlation.