Mini Curso - Introdução aos Métodos Quase-Experimentais em Microeconometria: Teoria e Pratica
Autor: Julio Cesar Araujo | Publicado: 2012-10-20T11:07:00-03:00

O Mini Curso ocorreu na UFRGS, não tive tempo para participar, mas tive o prazer de conhecer Caio Piza (Banco Inter-Americano de Desenvolvimento em Washington) em um seminário que ele apresentou dia 17.

Colarei abaixo o programa do mini - curso para que os interessados possam ter acesso, ao menos, as excelentes referências citadas por Caio.

Em meus 3 anos de UFRGS, é a primeira vez que vejo surgir um mini - curso tão completo e com carga horária compatível, Parabéns ao pessoal da secretaria (só lamento os horários coincidirem com algumas aulas). 



""Dia 17/10:
Motivação


Teoria: 10h as 12h 

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
O
Problema Fundamental da Inferência Causal: Holland (1986) e Lalonde (1986)

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Parâmetros
de Interesse em uma Avaliação: Duflo et al. (2007) e Ravallion (2008)

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Experimentos Aleatórios: Duflo et al. (2007)

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Overview dos Métodos Quase-Experimentais:
Ravallion (2001) e Duflo et al. (2007)



Aplicações no STATA: 14h as 13.50h

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Rápida introdução aos modelos de
váriavies dependentes binárias: Wooldridge (2003) 



Dia 18/10: Seleção
em Observáveis

Teoria: 10h as 12h

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
OLS
e CIA condition

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Propensity
Score Matching: Binary and Continuous Treatment Variable

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Reweighting
Regression

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
PSM
vs. OLS: Angrist and Pischke (2009) 

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Efeito
Distributivo do Tratamento: Firpo (2007)

Aplicações no STATA: 14h as 16h







Dia 19/10: Seleção
em Não-Observáveis:

Teoria: 10h as 12h

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Diferença-em-Diferenças
(Diff-in-Diff): Meyer et al. (1995), Duflo et al. (2007) e Ravallion (2008) 

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Diff-in-Diff
Matching Estimator: Blundell and Dias (2002) e Abadie (2005)

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Variavel
Instrumental e Regressao Discontinua: Imbens and Angrist (1994), Angrist et al.
(1996) e Hahn et al. (2001)

Aplicações no STATA: 14h as 16h



Referências para dia 17/10:

Duflo, E., R. Glennerster e M. Kremer. (2006),
Using Randomization in Development Economics Research: A Toolkit, Poverty
Action Lab, mimeo. 

Holland, P., (1986), Statistics and
Causal Inference, (with discussion), Journal
of the American Statistical Association, vol. 81, pp. 945-970.

Lalonde, R. (1986),
Evaluating the Econometric Evaluations of Training Programs, American Economic Review, vol.76, pp. 604-620.

Ravallion,
M. (2005), Evaluation Anti-Poverty Programs, in Handbook of Development Economics, Vol.4, edited by Evenson, R.E.
and Schultz, T. P. Amsterdam, North-Holland.&nbsp;


Ravallion, M., (2001), The Mystery of Vanishing
Benefits: An Introduction to Impact Evaluation, World Bank Economic Review, 15(1), 115-140.



Referências para dia 18/10:

Angrist, J.&amp; Pischke, J-S. Mostly Harmless Econometrics. Princeton
University Press, 2009. 

Becker,
S. and M. Caliendo, (2007), Sensitivity analysis for average treatment effects, Stata Journal, Volume 7, No. 1, pp.
71-83.

Buschinsky, M. (1998), Recent Advances in
Quantile Regression Model: A Practical Guideline For Empirical Research, Journal of Human Resources, vol. 33,
No.1, pp. 88-126. 

Dehejia, R., and S. Wahba, (1999), Causal Effects in
Non-experimental Studies: Re-evaluating the Evaluation of Training Programs, Journal of the American Statistical
Association, 94(448), pp.1053-1062.

Firpo, S. (2007), Efficient Semipametric
Estimation of Quantile Treatment Effects, Econometrica,
vol. 75, No. 1, pp. 259-276.

Fortin, N. M., and Lemieux, T. and Firpo, S.
(2009), Unconditional Quantile Regression, Econometrica,
vol. 77, No. 3, pp. 953-973.

Heckman, J., H. Ichimura, and P. Todd,
(1997), Matching as an Econometric Evaluation Estimator: Evidence from
Evaluating a Job Training Program, Review
of Economic Studies, 64(4), pp. 605-654.

Heckman, J., H. Ichimura, and P. Todd,
(1998) Matching as an Econometric Evaluation Estimator. The Review of Economic
Studies, 65(2), pp. 261-294.

Hirano,
K. and Imbens, G.W. (2001), Estimation of causal effects using propensity score
weighting: an application to data on right heart catheterization. Health Services and Outcomes Research
Methodology, Vol. 2, No.3-4, pp. 259-278.

Nichols,
A. (2007), Causal Inference with Observational Data, Stata Journal, vol. 7, No.4, pp. 507-541. 

Nichols, A. (2008), Erratum and discussion of propensity score reweighting, mimeo. 

Rosenbaum, P. R. and Rubin, D. B. (1983), The Central Role of the
Propensity Score in Observational Studies for Causal Effects, Biometrika, vol.70, n.1, p. 41-55.

Rubin, D. B., (1977), Assignment to a Treatment
Group on the Basis of a Covariate, Journal
of Educational Statistics, vol. 2, pp. 1-26.

Smith,
J. and P. Todd, (2005), Does matching overcome LaLonde's critique of
nonexperimental estimators?, Journal of
Econometrics, vol. 125(1-2), pp. 305-353.



Referências
para dia 19/10:

Abadie, A. (2005), Semiparametric
Difference-in-Differences Estimators, Review
of Economic Studies, vol. 72, pp.1-19. 

Attanasio,
O., Fitzsimons, E., Gomez, A., Gutierrez, M. I., Meguir, C., Mesnard, A.
(2010), Children's Schooling and Work in the Presence of a Conditional Cash
Transfer Programme in Rural Colombia. Economic Development and Cultural
Change, vol. 58, No.2, pp. 181-210.&nbsp; 

Angrist
J. D. and A. Krueger (1991), Does Compulsory School Attendance Affect Schooling
and Earnings?, Quarterly Journal of
Economics, vol. 106, pp. 979-1014.

Angrist, J., G. W. Imbens and D. Rubin, (1996),
Identification of Causal Effects Using Instrumental Variables, Journal of the American Statistical
Association, &nbsp;vol. 91, No.434, pp. 444-472.

Bertrand, M., Duflo, E. and Mullainathan, S.
(2004), How Much Should We Trust Differences-in-Differences Estimates?,&nbsp;The
Quarterly Journal of Economics, vol. 119, No.1, pp. 249-275.

Blundell, R. and Dias, M. C. (2002).
Alternative Approaches to Evaluation in Empirical Microeconomics, IFS working
paper CWP10/02. 

Card,
D. (1990), The Impact of the Mariel Boatlift on the Miami Labor Market, Industrial and Labor Relations Review,
vol. 44, 245-257.

Card,
D. and A. B. Krueger (1994), Minimum Wages and Employment: A Case Study of the
Fast-Food Industry in New Jersey and Pennsylvania, American Economic Review, vol. 84, 772-793.

Hahn,
J. P. Todd and H. Van Der Klaauw (2001),&nbsp;
Identification and Estimation of Treatment Effects with a Regression-Discontinuity
Design. Econometrica, vol. 69, pp.
201-209.

Imbens, G. W. &amp; J. D. Angrist. (1994),
Identification and estimation of local average treatment effects. Econometrica, vol. 62, pp. 467-475.

Imbens,G. W. e T.
Lemieux (2008), Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, vol. 142, issue
2: 615-635.

Meyer, Bruce D. (1995), Natural and
Quasi-Experiments in Economics, Journal
of Business &amp; Economic Statistics, vol.13, No.2, pp. 151-61.



Base de dados:

O site da UCLA (http://www.ats.ucla.edu/stat/stata/) contém ótimo material que pode
ser usado como introdução a vários commandos do STATA"