econometrics - intro to multiple regression

8/17/2019 Econometrics - Intro to Multiple Regression

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Chapter 6Chapter 6

Introduction to

Multiple Regression


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Outline

1. Omitted variable bias

2. Causality and regression analysis

3. Multiple regression and OLS4. Measures of fit

. Sampling distribution of the OLS estimator


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Omitted Variable Bias

(SW Section 6.1)


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Omitted variable bias, ctd.


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Te omitted !ariable bias "ormula#


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$igression on causalit% and

regression anal%sis


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Ideal Randomi&ed 'ontrolled

periment

• Ideal ! sub"e#ts all follo$ the treatment proto#ol % perfe#t#omplian#e& no errors in reporting& et#.'

• Randomized ! sub"e#ts from the population of interest are

randomly assigned to a treatment or #ontrol group (sothere are no #onfounding fa#tors)

• Controlled ! having a #ontrol group permits measuring thedifferential effe#t of the treatment

• Experiment ! the treatment is assigned as part of thee*periment! the sub"e#ts have no #hoi#e& so there is no+reverse #ausality, in $hi#h sub"e#ts #hoose the treatmentthey thin- $ill $or- best.


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Bac* to class si&e#


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Return to omitted variable bias


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Te +opulation Multiple Regression

Model (SW Section 6.,)


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Interpretation o" coe""icients in

multiple regression


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Te O-S stimator in Multiple

Regression (SW Section 6.)


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ample# te 'ali"ornia test score

data


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Multiple regression in ST/T/


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Measures o" 0it "or Multiple

Regression (SW Section 6.)


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SER and RMSE


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R , and 2

R


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R , and 2 ctd.2

R


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Measures of fit, ctd.


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Te -east S3uares /ssumptions "or

Multiple Regression (SW Section 6.4)


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/ssumption 51# te conditional mean o"

u gi!en te included X s is &ero.


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Te Sampling $istribution o" te

O-S stimator (SW Section 6.6)


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Multicollinearit%2 +er"ect and

Imper"ect (SW Section 6.7)


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Te dumm% !ariable trap


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Perfect multicollinearity, ctd.


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Imperfect multicollinearity


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Imperfect multicollinearity, ctd.

econometrics - intro to multiple regression

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