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

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Lecture 8: Midterm Review Econ 444, Winter 2010 Feb 8, 2010
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When : Feb 10, Wednesday. Where : In class, Arps 384. Time : 5:30 - 7:18 PM.
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Review of Statistics 1 Random Variable. 2 Expected value, Variance and Correlation. 3 Population vs Sample.
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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
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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)
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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
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Ch4 : The Classical Model Classical assumptions. Properties of the OLS estimate when classical assumptions are satisfied :
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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 = k- k SE ( 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. Condence Interval Estimation. CI k = k t c , 2 sided SE ( k )...
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Lecture8 - 1 Linear. 2 Unbiased. 3 Minimum Variance....

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