econometrics session 1 – introduction amine ouazad, asst. prof. of economics
TRANSCRIPT
![Page 1: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/1.jpg)
Econometrics
Session 1 – Introduction
Amine Ouazad,Asst. Prof. of Economics
![Page 2: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/2.jpg)
PRELIMINARIESSession 1 - Introduction
![Page 3: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/3.jpg)
Introduction
• Who I am• Arbitrage• Textbook• Grading• Homework• Implementation
Session 1• The two econometric problems• Randomization as the Golden Benchmark
Outline of the Course
![Page 4: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/4.jpg)
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.
![Page 5: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/5.jpg)
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.
![Page 6: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/6.jpg)
Textbooks
• *William H. Greene, Econometrics, 6th 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.
![Page 7: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/7.jpg)
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.
![Page 8: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/8.jpg)
Grading
• Exam: 60%
• Participation: 10%
• Homework: 30%
– One problem set in-between Econometrics A and B.
![Page 9: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/9.jpg)
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.
![Page 10: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/10.jpg)
Outline for Session 1Introduction
1. Correlation and Causation
2. The Two Econometric Problems
3. Treatment Effects
![Page 11: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/11.jpg)
1. CORRELATION AND CAUSATION
Session 1 - Introduction
![Page 12: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/12.jpg)
![Page 13: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/13.jpg)
1. The perils of confoundingcorrelation 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?
![Page 14: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/14.jpg)
![Page 15: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/15.jpg)
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?
![Page 16: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/16.jpg)
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?
![Page 17: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/17.jpg)
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:
![Page 18: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/18.jpg)
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.
![Page 19: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/19.jpg)
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)
![Page 20: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/20.jpg)
Randomizationas 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.
![Page 21: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/21.jpg)
With ignorability…
• If the treatment is ignorable (e.g. if the treatment has been randomly assigned to subjects) then– ATE = ATT = ATU
![Page 22: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/22.jpg)
Selection bias
• Why is there a selection bias?– In medecine, in economics, in
management?
1. Self-selection of subjects into the treatment.
2. Correlation between unobservables and observables, e.g. industry, gender, income.
![Page 23: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/23.jpg)
2. THE TWO ECONOMETRIC PROBLEMS
Session 1 - Introduction
![Page 24: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/24.jpg)
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.”
![Page 25: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/25.jpg)
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?
![Page 26: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/26.jpg)
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?
![Page 27: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/27.jpg)
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?
![Page 28: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/28.jpg)
LOOKING FORWARD:OUTLINE OF THE COURSE
Session 1 - Introduction
![Page 29: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/29.jpg)
Outline of the course
1. Introduction: Identification
2. Introduction: Inference
3. Linear Regression
4. Identification Issues in Linear Regressions
5. Inference Issues in Linear Regressions
![Page 30: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/30.jpg)
6. Identification in Simultaneous Equation Models
7. Instrumental variable (IV) estimation
8. Finding IVs: Identification strategies
9. Panel data analysis
![Page 31: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/31.jpg)
10.Bootstrap
11.Generalized Method of Moments (GMM)
12.GMM: Dynamic Panel Data estimation
13.Maximum Likelihood (ML): Introduction
14.ML: Probit and Logit
![Page 32: Econometrics Session 1 – Introduction Amine Ouazad, Asst. Prof. of Economics](https://reader036.vdocuments.net/reader036/viewer/2022062511/551be913550346c3588b61c5/html5/thumbnails/32.jpg)
15.ML: Heckman selection models
16.ML: Truncation and censoring
+ Exercise/Review session
+ Exam