Lecture8

# Lecture8 - 1 Linear 2 Unbiased 3 Minimum Variance...

This preview shows pages 1–10. Sign up to view the full content.

Lecture 8: Midterm Review Econ 444, Winter 2010 Feb 8, 2010

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
When : Feb 10, Wednesday. Where : In class, Arps 384. Time : 5:30 - 7:18 PM.
Review of Statistics 1 Random Variable. 2 Expected value, Variance and Correlation. 3 Population vs Sample.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Ch1 : An Overview of the Regression Analysis Population Regression vs Estimated (Sample) Regression. E ( Y i | X i ) = β 0 + β 1 X i : Population Regression ˆ Y i = ˆ β 0 + ˆ β 1 X i : Sample Regression Stochastic error vs Residual. ± i = Y i - E ( Y i | X i ) : Stochastic Error e i = Y i - ˆ Y i : Residual
Ch2 : Ordinary Least Squares (OLS) OLS minimizes N i = 1 e 2 i by choice of ˆ β s How to calculate ˆ β 0 and ˆ β 1 using a simple data (without Eviews). Properties of OLS : 1 ¯ e = 1 N N X i = 1 e i = 0 . (1) 2 ¯ Y = ˆ β 0 + ˆ β 1 · ¯ X . (2)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Ch2 : Ordinary Least Squares (OLS) Multiple Regression : Y i = β 0 + β 1 X 1 i + ... + β K X Ki + ± i (3) Interpreting these coefﬁcients. R 2 vs ¯ R 2
Ch4 : The Classical Model Classical assumptions. Properties of the OLS estimate when classical assumptions are satisﬁed :

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 1 Linear. 2 Unbiased. 3 Minimum Variance. Gauss-Morkov Theorem. Ch5 : Hypothesis Testing Null Hypothesis vs Alternative Hypothesis. Two sided vs One sided tests. t-test. t k = ˆ β k-β k SE ( ˆ β k ) follows t-distribution with N-k-1 degrees of freedom. | t | > t c then reject the H . Else accept it. Ch5 : Hypothesis Testing Possible errors in hypothesis testing. Testing linear restrictions using t-test. Ch5 : Hypothesis Testing F-test. F = R 2 / k ( 1-R 2 ) / N-k-1 follows F with k degrees of freedom for the numerator and N-k-1 degrees of freedom for the denominator. F > F c then reject the H . Else accept it. Conﬁdence Interval Estimation. CI ˆ β k = ˆ β k ± t c , 2 sided × SE ( ˆ β k )...
View Full Document

{[ snackBarMessage ]}

### Page1 / 10

Lecture8 - 1 Linear 2 Unbiased 3 Minimum Variance...

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online