## ECONOMETRICS

## Program

### Updated A.Y. 2019-2020

**OVERVIEW AND PREREQUISITIES**

In line with the educational objectives of the programme, the aim of this course is to introduce students to the main econometric tools essential for conducting quantitative analyses in the economic field. To this aim, several empirical applications, mostly implemented in Stata, will be discussed. The course consists of eighteen theoretical lectures (1 hour and 45 minutes each) and six practice classes, three of which will be given in the lab. Students should have completed Mathematics (8011190) and Statistics (8010848). In particular, they will need to be able to work with linear algebra and basic asymptotic theory (Law of large numbers and central limit theory for independent and identically distributed observations).

**OUTLINE**

The structure of economic data

Linear regression model with cross-sectional data:

- Ordinary Least Squares (OLS) estimation

- Mechanics, model specification and interpretation

- OLS Sampling Properties

- Violation of the ideal conditions for OLS

- Hypothesis testing and model selection

- Instrumental Variables (IV) estimation

- Generalized Method of Moments (GMM) estimation

- Control function approach to endogeneity

Linear (static) unobserved-effects regression model with panel data:

- Pooled OLS

- Fixed-effects and first-difference estimation

- Random-effects estimation

- Comparison of estimators

**TEXTBOOK AND MATERIAL**

Wooldridge J.M., (2016), Introductory Econometrics: A Modern Approach, 6th ed., Cengage Learning.

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

The main references are Wooldridge (2016) and Wooldridge (2010). They will be complemented by some chapters from Peracchi (2001). Lecture slides and Stata files will be posted on the course web site.

**READING LIST**

Angrist J., and Krueger A., (2001), "Instrumental variables and the search for identification: From supply and demand to natural experiment", Journal of Economic Perspectives, 15: 69–85.

Wooldridge J.M., (2001), "Applications of generalized method of moments estimation", Journal of Economic Perspectives, 15: 87–100.

Wooldridge J.M., (2015), "Control Function Methods in Applied Econometrics", Journal of Human Resources, 50(2): 420–445.

**USEFUL REFERENCES**

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

Cameron A.C., and Trivedi P.K., (2005), Microeconometrics. Methods and Applications, Cambridge University Press, New York.

Cook R.D., and Weisberg S., (1982), Residuals and Influence in Regression, Chapman and Hall, New York.

Hall A.H., (2005), Generalized Method of Moments, Oxford University Press, Oxford.

Hansen, B.E., (2019), Econometrics, mimeo (downloadable at https://www.ssc.wisc.edu/~bhansen/econometrics/Econometrics.pdf).

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

Stock J.H., and Watson M.W., (2015), Introduction to Econometrics (updated 3rd ed.), Pearson, Hoboken (NJ).

White H., (2001), Asymptotic Theory for Econometricians (2nd ed.), Academic Press, San Diego (CA).

Petersen K.B., and Pedersen M.S., (2012), The Matrix Cookbook, mimeo (downloadable at https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf).

Suggestions for further reading will be provided in class.

**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 linear 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.