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Unformatted text preview: Multivariate OLS  II 73261 Econometrics September 8 Wooldridge 3.13.2, 6.3 Gujarati 13.9 p. 2 © CMU / Y. Kryukov Announcements Homework 1 – due now Homework 2 is on Blackboard Due next Wednesday after next Tuesday office hours moved to 1:403:40 73261 Multivariate OLS  II p. 3 © CMU / Y. Kryukov Plan: Multivariate regression Last time: Basics (Wooldridge 3.13.2) Adjusted R 2 (Wooldridge 6.3) Today: Distribution and selection Distribution, mean & variance (Wooldridge 3.33.4) Variable Selection (Wooldridge 6.3, Gujarati 13.9) 73261 Multivariate OLS  II p. 4 © CMU / Y. Kryukov Recap: Multivariate OLS Population: y = β + β 1 x 1 + … + β k x k + u = x β + u Sum of squared errors: Σ ( y − β − β 1 x 1 − … − β k x k ) 2 = ( Y − X β )’( Y − X β ) Minimizing it: Y X X X ' ) ' ( ˆ 1 − = β 73261 Multivariate OLS  II p. 5 © CMU / Y. Kryukov Recap: population vs. estimates Population: true β and distribution of u x has a distribution too, but we observe it Data: N draws, stacked into matrices X & Y We know X and we condition on it as before We do not know U = [ U 1 , …, U N ] ’ , so it remains a random variable Estimate : function of X & Y It is a vector of random variables since Y = X β + U β ˆ 73261 Multivariate OLS  II p. 6 © CMU / Y. Kryukov Distribution of...
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This note was uploaded on 01/21/2011 for the course ECON 73261 taught by Professor Kyrkv during the Fall '09 term at Carnegie Mellon.
 Fall '09
 Kyrkv
 Econometrics

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