This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: 1 IV. Multiple Regression . A. Introduction B. CRM C. Estimation D. Interpretation of Parameter Estimates E. Properties of Estimators. F. Estimator for σ 2 and Variances for G. Inferences in Multiple Regression H. Goodness of Fit I. Analysis of Variance ˆ s β Elasticities – another interpretation of results. • Elasticity – the % change in quantity due to a 1% change in price, or income. • From micro: • For the estimated regression model: E. Properties of OLS Estimators 1. Linear: The OLS estimators are still linear estimators. 2. Unbiased: If CRMAs # 1 – 3 are correct, the OLS estimators are unbiased estimators. 3. Minimum Variance: If CRMAs # 1 – 5 are correct, the OLS estimators are the Best Linear Unbiased Estimators. (Gauss-Markov Theorem) Draw a graph illustrating properties 2 & 3. 2 F. Estimator for σ 2 and Variances for 1. Recall: E[u 2 ] = σ 2 ; an average of squared disturbances....
View Full Document
- Winter '10
- Regression Analysis, #, Statistical hypothesis testing, 1%, 12%, OLS estimators