Lecture_7__Prof._Arkonac's_Slides_(Ch_6_-_Ch_7.2_)

Lecture_7__Prof._Arkonac's_Slides_(Ch_6_-_Ch_7.2_) -...

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Multiple Regression I (cont’) & II (Fall 2010) Lecture 7 Prof: Seyhan Erden Arkonac, PhD Problem Set 2 is due TODAY! Answers to PS#2 will be posted on Wed Sept 29th. Problem Set 3 will be posted today, it is due on Tues. Oct. 5 th . 1
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TA Information: Naihobe Gonzalez E-mail: ndg2109@columbia.edu Office Hours: Thurs 12-1 (Uris Library), Recitation: Thurs 11-11:50 (PUP 424) TA Information: Ju Hyun Kim E-mail: jk3201@columbia.edu Office Hours: Recitation: Fri 2:10-3PM(PUP 424), Office Hours: Fri 3:10-4:10PM(Lehman) TA Information: WooRam Park E-mail: wp2135@columbia.edu 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: rh2346@columbia.edu Office Hours: Recitation: Thursday 12:00-12:50 404IAB; Office Hour: Wednesday 1-2 1006A IAB TA Information: Shreya Agarwal E-mail: sa2628@columbia.edu 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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Where we stopped last time, and what will we do today? Regression when X is a binary (dummy) variable (i.e. X=0 or X=1) Heteroskedasticity and Homoskedasticity (variance of the error term is constant? Or not?) The theoretical foundation of the OLS (not in detail!) Omitted variable bias THE MULTIPLE REGRESSION MODEL 3
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4 Districts with fewer English Learners have higher test scores Districts with lower percent EL ( PctEL ) have smaller classes Among districts with comparable PctEL , the effect of class size i small (recall overall “test score gap” = 7.4)
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5 Digression on causality and regression analysis What do we want to estimate? What is, precisely, a causal effect? The common-sense definition of causality isn’t precise enough for our purposes. In this course, we define a causal effect as the effect that is measured in an ideal randomized controlled experiment .
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6 Ideal Randomized Controlled Experiment Ideal : subjects all follow the treatment protocol – perfect compliance, no errors in reporting, etc.! Randomized : subjects from the population of interest are randomly assigned to a treatment or control group (so there are no confounding factors) Controlled : having a control group permits measuring the differential effect of the treatment Experiment : the treatment is assigned as part of the experiment: the subjects have no choice, so there is no “reverse causality” in which subjects choose the treatment they think will work best.
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7 Back to class size: Conceive an ideal randomized controlled experiment for measuring the effect on Test Score of reducing STR How does our observational data differ from this ideal? The treatment is not randomly assigned Consider PctEL – percent English learners – in the district. It plausibly satisfies the two criteria for omitted variable bias: Z = PctEL is: 1. a determinant of Y ; and 2. correlated with the regressor X .
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This note was uploaded on 11/10/2011 for the course ECON 3142 taught by Professor Arkonac during the Spring '11 term at Columbia.

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Lecture_7__Prof._Arkonac's_Slides_(Ch_6_-_Ch_7.2_) -...

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