Session 1 Introduction Amine Ouazad Asst Prof of Economics Preliminaries Session 1 Introduction Introduction Who I am Arbitrage Textbook Grading Homework Implementation Session 1 ID: 272450
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Slide1
Econometrics
Session 1 – Introduction
Amine Ouazad,
Asst. Prof. of EconomicsSlide2
Preliminaries
Session 1 - IntroductionSlide3
Introduction
Who I am
Arbitrage
Textbook
Grading
Homework
Implementation
Session 1
The two econometric problems
Randomization as the Golden Benchmark
Outline of the CourseSlide4
Who I am
Applied empirical economist.
Work on urban economics, economics of education, applied econometrics in accounting.
Emphasis on the
identification of causal effects
.
Careful empirical work: clean data work, correct identification of causal effects.
Large datasets:
+100 million observations, administrative datasets, geographic information software.
Implementation of econometric procedures in
Stata
/Mata.Slide5
Trade-offs
Classroom is heterogeneous.
In tastes, mathematics level, needs, prior knowledge.
Different fields have different habits.
E.g. “endogeneity” is not an issue/the same issue in OB, Finance, Strategy, or TOM.
Conclusion:
Course provides a particular spin on econometrics, with mathematics when needed, applications.
This is a
difficult
course, even for students with a prior course in econometrics.Slide6
Textbooks
*
William H. Greene, Econometrics, 6
th
edition.
Jeffrey Wooldridge, Econometrics of Cross Section and Panel Data.
Joshua Angrist and Jorn Steffen Pischke, Mostly Harmless Econometrics.
Applied Econometrics using Stata, Cameron et al.Slide7
Prerequisites
I assume you know:
Statistics
Random variables.
Moments of random variables (mean, variance, kurtosis, skewness).
Probabilities.
Real analysis
Integral of functions, derivatives.
Convergence of a function at x or at infinity.
Matrix algebra
Inverse, multiplication, projections.Slide8
Grading
Exam: 60%
Participation: 10%
Homework: 30%
One problem set in-between Econometrics A and B.Slide9
Implementation
STATA version 12.
License for PhD students. Ask IT. 5555 or Alina Jacquet.
Interactive mode, Do files, Mata programming.
Compulsory for this course.
MATLAB, not for everybody.
Coding econometric procedures yourself, e.g. GMM.Slide10
Outline for Session 1
Introduction
Correlation and Causation
The Two Econometric Problems
Treatment EffectsSlide11
1. Correlation and Causation
Session 1 - IntroductionSlide12Slide13
1. The perils of confounding
correlation and causation
How can we boost children’s reading scores?
Shoe
size is correlated with IQ
.
Women earn less than men
.
Sign of discrimination?
Health is negatively correlated with the number of days spent in hospital.
Do hospitals kill patients?Slide14Slide15
Potential outcomes framework
A.k.a
the “
Rubin causality model
”.
Outcome with the treatment Y(1), outcome without the treatment Y(0).
Treatment status D=0,1.
FUNDAMENTAL PROBLEM OF ECONOMETRICS:
Either
Y(1) or Y(0) is observed, or, equivalently, Y=Y(1) D + Y(0) (1-D) is observed.
What would have happened if a given subject had received a different treatment?Slide16
Naïve estimator of
the treatment effect
D
=E(Y|D=1) – E(Y|D=0).
Does that identify any relevant parameter
?
Notice that:
D
= E(Y|D=1
) – E(Y|D=0
)
= E(Y(1)|D=1)-E(Y(0)|D=0)
What are we looking for?Slide17
Ignorable Treatment (Rubin 1983)
Assume Y(1),Y(0)
D.
Then E(Y(0)|D=1)=E(Y(0)|D=0)=E(Y(0)).
Similarly for Y(1).
Then:Slide18
Another Interpretation
Assume Y(D)=
a+bD+
e
.
e is the “
unobservables
”.
The naïve estimator D=
b+E
(
e
|D
=1)-E(
e
|D
=0).
Selection bias: S=E(
e
|D
=1)-E(
e
|D
=0).
Overestimates the effect if S>0
Underestimates the effect if S<0.Slide19
Definitions
Treatment Effect.
Y(1)-Y(0)
Average Treatment Effect.
E(Y(1
)-Y(0
))
Average Treatment on the Treated.
E(Y(1
)-Y(0
)|D=1)
Average Treatment on the Untreated.
E(Y(1
)-Y(0
)|D=0)Slide20
Randomization
as the Golden Benchmark
Effect of a medical treatment.
Treatment and control group.
Randomization of the assignment to the treatment and to the control.
Why randomize?
… effect of jumping without a parachute on the probability of death.Slide21
With
ignorability
…
If the treatment is ignorable (e.g. if the treatment has been randomly assigned to subjects) then
ATE = ATT = ATUSlide22
Selection bias
Why is there a selection bias?
In medecine, in economics, in management?
Self-selection of subjects into the treatment.
Correlation between unobservables and observables, e.g. industry, gender, income.Slide23
2. The Two econometric problems
Session 1 - IntroductionSlide24
2. The Two Econometric Problems
Identification and Inference
“Studies of
identification
seek to characterize the conclusions that could be drawn if one could use the
sampling process
to obtain
an unlimited number of observations
.”
“Studies of
statistical inference
seek to characterize the generally weaker conclusions that can be drawn from a
finite number of observations
.”Slide25
Identification vs inference
Consider a survey of a random subset of 1,302 French individuals.
Identification
:
Can you identify the average income in France?
Inference
:
How close to the true average income is the mean income in the sample?
i.e. what is the confidence interval around the estimate of the average income in Singapore?Slide26
Identification vs inference
Consider a lab experiment with 9 rats, randomly assigned to a treatment group and a control group.
Identification
:
Can you identify the effect of the medication on the rats using the random assignment?
Inference
:
With 9 rats, can you say anything about the effectiveness of the medication?Slide27
This session
This session
has focused
on identification.
i.e. I assume we have a potentially infinite dataset.
I focus on the conditions for the identification of the causal effect of a variable.
Next session:
what problems appear because we have a limited number of observations?Slide28
Looking forward:
Outline of the course
Session 1 - IntroductionSlide29
Outline of the course
Introduction: Identification
Introduction: Inference
Linear Regression
Identification Issues in Linear Regressions
Inference Issues in Linear RegressionsSlide30
Identification in Simultaneous Equation Models
Instrumental variable (IV) estimation
Finding IVs: Identification strategies
Panel data analysisSlide31
Bootstrap
Generalized Method of Moments (GMM)
GMM: Dynamic Panel Data estimation
Maximum Likelihood (ML): Introduction
ML: Probit and LogitSlide32
ML: Heckman selection model
s
ML: Truncation and censoring
+ Exercise/Review session
+ Exam