Mini Curso - Introdução aos Métodos Quase-Experimentais em Microeconometria: Teoria e Pratica

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

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

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

-          Experimentos Aleatórios: Duflo et al. (2007)

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

Aplicações no STATA: 14h as 13.50h

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

Dia 18/10: Seleção em Observáveis

Teoria: 10h as 12h

-          OLS e CIA condition

-          Propensity Score Matching: Binary and Continuous Treatment Variable

-          Reweighting Regression

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

-          Efeito Distributivo do Tratamento: Firpo (2007)

Aplicações no STATA: 14h as 16h

Dia 19/10: Seleção em Não-Observáveis:

Teoria: 10h as 12h

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

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

-          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. 

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.& 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. 

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,  vol. 91, No.434, pp. 444-472.

Bertrand, M., Duflo, E. and Mullainathan, S. (2004), How Much Should We Trust Differences-in-Differences Estimates?, 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),  Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Econometrica, vol. 69, pp. 201-209.

Imbens, G. W. & 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 & 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"