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Econometrics Econometrics

Econometrics - PowerPoint Presentation

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Econometrics - PPT Presentation

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

identification treatment econometrics session treatment identification session econometrics introduction effect inference average econometric income problems selection assume random data

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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 - IntroductionSlide12
Slide13

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?Slide14
Slide15

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