ie_Slide03_2 - Introductory Econometrics ECON2206/ECON3209...

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Introductory Econometrics ECON2206/ECON3209 Slides03_continued Multicollinearity and Misspecification Lecturer: Rachida Ouysse ie_Slides03_2 R. Ouysse, School of Economics, UNSW 1
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3. Multiple Regression Model: Estimation (Ch3) • Irrelevant “explanatory” variables – What if an irrelevant x is included? • “irrelevant” means the population coefficient of that variable is 0. • as the OLS estimators are unbiased, the estimate of that coefficient will “typically” be near 0. • the inclusion of irrelevant variables has undesirable effects on the variances of the OLS estimators. g irrelevant in the model eg. x 3 is irrelevant in the model y = β 0 + β 1 x 1 + β 2 x 2 + 0 x 3 + u , e OLS estimator is unbiased: ˆ the OLS estimator is unbiased: in particular, . ,..., , , ) ( k j E j j 1 0   . ) ˆ ( 0 3 3   E ie_Slides03_2 R. Ouysse, School of Economics, UNSW 2
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3. Multiple Regression Model: Estimation (Ch3) • Omitted explanatory variables – What if a relevant x is omitted? • the OLS estimators will generally be biased. • the direction and size of bias depend on how the omitted is related to the included. – Example. When
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This note was uploaded on 02/29/2012 for the course ECON 2206 taught by Professor Yang during the One '11 term at University of New South Wales.

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ie_Slide03_2 - Introductory Econometrics ECON2206/ECON3209...

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