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Program

Updated A.Y. 2019-2020

OVERVIEW AND PREREQUISITIES

The aim of this course is to deepen the topics covered in the econometrics course by introducing the student to the state-of-the-art techniques for estimating causal effects, the dynamic linear panel data model and to the limited dependent variable models for both cross-sectional and longitudinal data. The course consists of eighteen theoretical lectures, with applications based on economic micro-data. Students should have completed Mathematics (8011190), Statistics (8010848) and Econometrics (8011571). A good knowledge of Stata and/or R is required.

 

OUTLINE

Dynamic linear panel data models

Cross-sectional and panel data models for discrete and limited dependent variables:

- Binary outcomes models
- Multinomial outcomes models
- Count data models
- Models for truncated/censored data and sample selection models

Estimating Average Treatment Effects:

- Setup and main assumptions: treatment effects and selection bias
- Matching and propensity score estimators
- Differences-in-differences estimators
- Instrumental variables estimators
- Regression discontinuity

 

TEXTBOOK AND MATERIAL

Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).

Lecture slides will be posted on the course web site.

 

READING LIST

Arellano M., Bond S., (1991), Some tests of specification for panel data: Monte carlo evidence and an application to employment equations, Review of Economic Studies, 58:277-297.

Blundell R., Bond S., (1998), Initial conditions and moment restrictions in dynamic panel data models, Journal of Econometrics, 87:115-143.

Bond S., (2002), Dynamic panel data models: A guide to micro data methods and practice, Portuguese Economic Journal, 1(2):141-162 

Gourieroux C., Monfort A., Renault E., Trognon A., (1987), Generalised residuals, Journal of Econometrics, 34:5-32

Imbens G.W., Lemieux T., (2008), Regression discontinuity designs: A guide to practice, Journal of Econometrics, 142(2):615-635.

Lewbel A., Dong Y., Yang T.T., (2012), Comparing features of convenient estimators for binary choice models with endogenous regressors, Canadian Journal of Economics, 45(3): 809-829.

Roodman D.M., (2009), A note on the theme of too many instruments, Oxford Bulletin of Economics and Statistics, 71:135-158.

Roodman, D.M, (2009), How to do xtabond2: An Introduction to Difference and System GMM in Stata. The Stata Journal, 9(1):86-136.

Wilde, J. (2000), Identification of multiple equation probit models with endogenous dummy regressors, Economics Letters, 69:309-312

Wooldridge, J.M. (2005), Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity, Journal of Applied Econometrics, 20: 39-54.

Suggestions for further reading will be provided in class.

 

USEFUL REFERENCES

Hsiao C., Analysis of Panel Data. Cambridge University Press, New York, NY, 3rd edition, 2014.

Angrist J.D., and Pischke J.-S., (2009), Mostly Harmless Econometrics: An Empiricists’s Companion, Princeton University Press, Princeton.

Peracchi F., (2001), Econometrics, Wiley, Chichester (UK).

Train, K. E., (2009), Discrete Choice Methods with Simulation, 2nd ed. New York: Cambridge University Press. Downloadable here

 

KNOWLEDGE AND UNDERSTANDING

Based on a systematic and conscious understanding of the discussed techniques, students should be able to elaborate original ideas to answer economic questions of interest.

APPLYING KNOWLEDGE AND UNDERSTANDING

Based on the analytical tools and the knowledge acquired through theoretical and practice sessions, students should be able to apply appropriately the discussed techniques using statistical software such as Stata and/or R. In particular, they will need to be able to correctly specify a regression model, choose the most appropriate estimation approach and correctly interpret the empirical results.

MAKING JUDGEMENTS

Students should be able to autonomously integrate the acquired knowledge in order to manage complex empirical analyses, by appropriately defining the objectives, by formulating hypotheses, by autonomously searching for the information and data necessary for carrying out the analysis, by motivating the choice of the most appropriate methodology and by extracting useful strategic indications and/or policy implications based on the empirical results.

COMMUNICATION SKILLS

Students should to be able to present the results of their own elaborations and analyses both to an expert and non-experts audience.

LEARNING SKILLS 

Student should be able to study independently, developing the learning skills needed to tackle more advanced econometrics courses or to undertake the quantitative analyses required in other courses or for the final dissertation.