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Lecture_8_Prof._Arkonac's_Slides_(Ch_6.7_-_Ch_7.7)

Lecture_8_Prof._Arkonac's_Slides_(Ch_6.7_-_Ch_7.7) -...

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Multiple Regression III (Fall 2010) Lecture 8 Prof: Seyhan Erden Arkonac, PhD Problem set #3 is posted, it is due on Tues Oct. 5 th (Warning: This ps is longer than the previous ones!!!). Answer to Problem set #2 is posted. 1
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TA Information: Naihobe Gonzalez E-mail: [email protected] Office Hours: Thurs 12-1 (Uris Library), Recitation: Thurs 11-11:50 (PUP 424) TA Information: Ju Hyun Kim E-mail: [email protected] Office Hours: Recitation: Fri 2:10-3PM(PUP 424), Office Hours: Fri 3:10-4:10PM(Lehman) TA Information: WooRam Park E-mail: [email protected] Office Hours: Office hours : Thurs 2:00~3:00 IAB 1006A Recitation Thurs 3:10~4:00 IAB 403 TA Information: Ran Huo E-mail: [email protected] Office Hours: Recitation: Thursday 12:00-12:50 404IAB; Office Hour: Wednesday 1-2 1006A IAB TA Information: Shreya Agarwal E-mail: [email protected] Office Hours: Mon 12:30pm - 1:30 pm (Uris Library Common Area) Recitation: Fri 12:00pm - 12:50pm (Schermerhorn Extension 558) 2
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3 The Sampling Distribution of the OLS Estimator (SW Section 6.6) Under the four Least Squares Assumptions, The exact (finite sample) distribution of 1 ˆ has mean 1 , var( 1 ˆ ) is inversely proportional to n ; so too for 2 ˆ . Other than its mean and variance, the exact (finite- n ) distribution of 1 ˆ is very complicated; but for large n 1 ˆ is consistent: 1 ˆ p 1 (law of large numbers) 1 1 1 ˆ ˆ ( ) ˆ var( ) E is approximately distributed N (0,1) (CLT) So too for 2 ˆ ,…, ˆ k Conceptually, there is nothing new here!
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