MICROECONOMETRICS

The course is an integral part of the Econometrics sequence provided to Ph.D. students in Economics. The theoretical foundations of the Econometrics EM2Q05 course are deepened and specialized in the microeconometric field. Numerous applications of the analytical tools presented during the course will then be proposed. Students are required to use the STATA software

This course is aimed at providing students with the skills required to undertake independent applied research using modern microeconometrics methods.

The student must know the contents of the EM2Q05 course, or more generally the basic elements of an advanced econometric theory course. Specifically, chapters 1 to 5 of the text Davidson, Russell, and James G. MacKinnon. Econometric theory and methods.New York: Oxford University Press, 2004

• Endogeneity in cross-sectional models: Instrumental Variables and GMM
• Cluster-Robust Inference and bootstrap methods
• Model specification: non-linear transformations in variables, selecting regressors, testing model specification, multicollinearity
• Panel data estimations, basics: pooled OLS, First Differences, Fixed effects, Random Effects, Mundlak approach
• Endogeneity in panel data models.
• Dynamic panel data models
• Binary choice models: latent variables and Random Utility Theory; Linear probability models; Probit and Logit
• Binary choice models with endogenous regressors
• Binary choice models with panel data
• Spatial Econometrics

The reference textbooks are
1. Cameron and Trivedi (2005) “Microeconometrics: Methods and Applications” Cambridge Univ Press, Cambridge, UK.
2. Wooldridge (2010) “Econometric Analysis of Cross Section and Panel Data”, 2nd edition, MIT press, USA

Other material will be taken from
1. Davidson, Russell, and James G. MacKinnon. Econometric theory and methods. New York: Oxford University Press, 2004.
2. Greene, William. H.(2003) Econometric Analysis. New Jersey, ua: Prentice Hall (2003): 135-145.
3. Baltagi, B. H. (Ed.). (2008). A companion to theoretical econometrics. John Wiley & Sons.
4. Verbeek, M. (2005). A modern guide to econometrics. Wiley.
5. Hsiao (1986) Analysis of panel data, Cambridge University Press, Cambridge
6. Anselin, Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers, 1988.

Grading will be based on three homeworks (60%) and one final take home exam (40%). Both the homeworks and the take home exam will contain theory questions and empirical exercises to be performed with STATA. Working in groups is strongly encouraged: homeworks must be done in couples, but couples must change over assignments. The take home exam must be done individually.

Lectures and home assignments. Students are urged to actively participate in class discussion.

The textbooks will be integrated with a list of papers during the course. Non exhaustive list:

1. Anderson, T. W., & Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American statistical Association, 76(375), 598-606.
2. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The review of economic studies, 58(2), 277-297.
3. Blundell, R., & MaCurdy, T. (2000). Labor Supply," Handbook of Labor Economics, O. Ashenfelter and D. Card, eds.
4. Brambor, Clark, Golder (2006) Understanding Interaction Models: Improving Empirical Analyses", Political Analysis 14:63-82
5. Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 239-253.Nickell (1981)
6. Bun, M. J., & Kiviet, J. F. (2006). The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models. Journal of econometrics, 132(2), 409-444.Rivers and Vuong (1988)
7. Burbidge, J. B., Magee, L., and Robb, L. A. (1988) Alternative transformations to handle extreme values of the dependent variable, Journal of the American Statistical Association 83, 123-127
8. Butcher, Kristin F., and Anne Case. "The effect of sibling sex composition on women's education and earnings." The Quarterly Journal of Economics (1994): 531-563.
9. Cameron, A. C., & Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. Journal of Human Resources, 50(2), 317-372.
10. Elhorst, J. P. (2003). Specification and estimation of spatial panel data models. International regional science review, 26(3), 244-268.
11. Evans, W. N., & Montgomery, E. (1994). Education and health: where there's smoke there's an instrument (No. w4949). National Bureau of Economic Research.
12. Fernández-Val, I. (2009). Fixed effects estimation of structural parameters and marginal effects in panel probit models. Journal of Econometrics, 150(1), 71-85.
13. Hausman, J. "Specification tests in econometrics." Econometrica (1978): 1251-1271.
14. Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica: Journal of the econometric society, 153-161.
15. Heckman, Lochner and Todd (2003) "Fifty Years of Mincer Earnings Regressions". NBER wp 9732
16. Kelejian, H. H., & Prucha, I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. The Journal of Real Estate Finance and Economics, 17(1), 99-121
17. Kennedy, P. E. (2002). Sinning in the basement: What are the rules? The ten commandments of applied econometrics. Journal of Economic Surveys, 16, 569-589.
18. Lam, David, and Robert F. Schoeni. "Effects of family background on earnings and returns to schooling: evidence from Brazil." Journal of political economy (1993): 710-740.
19. Millo, G., & Pasini, G. (2010). Does Social Capital Reduce Moral Hazard? A Network Model for Non‐Life Insurance Demand*. Fiscal Studies, 31(3), 341-372.
20. Moulton, B. (1990) An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units, The Review of Economics and Statistics, Vol. 72, No. 2, pp. 334-338
21. Mundlak, Yair. "On the pooling of time series and cross section data." Econometrica: journal of the Econometric Society (1978): 69-85.
22. Sanderson, E., & Windmeijer, F. (2013). A weak instrument F-test in linear IV models with multiple endogenous variables (No. CWP58/13). CEMMAP working paper, Centre for Microdata Methods and Practice.

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This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development