Facoltà di Economia

Learning Objectives

Prerequisites

Program

Books

Bibliography

Teaching methods

Exam Rules

Learning Objectives

Prerequisites

Program

Books

Bibliography

Teaching methods

Exam Rules

Learning Objectives

Prerequisites

Program

Books

Bibliography

Teaching methods

Exam Rules

Aggiornato A.A. 2021-2022

OVERVIEW AND PREREQUISITIES

The aim of this course is to provide a survey of state-of-the-art microeconometric techniques. We will cover linear models for longitudinal data and a variety of causal inference designs and methods. We will discuss the strengths and weaknesses of these tools using economic applications.

A willingness to work hard on possibly unfamiliar material is key and, in addition to the material covered in the Mathematics (8011190), Statistics (8010848) and Econometrics (8011571) courses, I also expect that you are reasonably proficient with the Stata statistical software.

OUTLINE

- Causal inference: main assumptions and estimands
- Regression (reloaded)
- Propensity score estimators
- Matching

• Matching on the PS

- Regression discontinuity

• Sharp design
• Fuzzy design

- Panel data

• Pooling
• Random-effects
• Fixed-effects (within group and first-differences estimators)
• Interactive fixed-effects
• Correlated random-effects
• Hausman type tests

- Difference-in-differences
- (Generalized) Synthetic controls

TEXTBOOKS AND MATERIAL

The main references are Wooldridge (2010) and Cunningham (2021). For further study in microeconometrics I suggest: Hansen (2021), Hsiao (2014), Cameron and Trivedi (2005) and Peracchi (2001). Angrist and Pischke (2009) for applied issues. Lecture notes, slides and Stata codes will be posted in the material section. Suggestions for further reading will be provided in class. 

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

Hansen B.E., (2020), Econometrics, download the book here.

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

Cameron C., Trivedi P.K., (2005), Microeconometrics: methods and applications, Cambridge University Press.

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

Cunningham S., (2021) Causal Inference: The Mixtape. Yale University Press.

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

Aggiornato A.A. 2020-2021

OVERVIEW AND PREREQUISITIES

The aim of this course is to provide a survey of state-of-the-art microeconometric techniques. We will cover limited dependent variable models, linear models for longitudinal data and a variety of causal inference designs and methods. We will discuss the strengths and weaknesses of these tools using economic applications.

A willingness to work hard on possibly unfamiliar material is key and, in addition to the material covered in the Mathematics (8011190), Statistics (8010848) and Econometrics (8011571) courses, I also expect that you are reasonably proficient in the statistical software Stata or R.

OUTLINE

Linear panel data models:

- Pooling
- Random-effects
- Fixed-effects (within group and first-differences estimators)
- Interactive fixed-effects
- Correlated random-effects
- Hausman type tests

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 (optional)

Estimating Average Treatment Effects:

- Treatment effects and selection bias
- Matching and propensity score estimators
- IV reloaded: the Local Average Treatment Effect estimator
- Regression discontinuity
- Differences-in-differences estimators
- Generalized synthetic control

TEXTBOOK AND MATERIAL

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

Lecture slides and a reading list will be posted on the course web site.

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.

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 practical sessions, students should be able to apply appropriately the discussed techniques using statistical software such as Stata and/or R.

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.

Aggiornato A.A. 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.

Aggiornato A.A. 2018-2019

Overview and requirements

The course provides a detailed overview of microeconometric methods for both cross-sectional and panel data. It focuses on nonlinear and advanced panel data models, with special emphasis on intuitions and applications. The relevant methodologies are discussed in class and then implemented during lab sessions, where students will have the opportunity to gain hands-on experience using the Stata statistical software. At the end of the course students should be able to specify the appropriate econometric model for investigating the problem under study and draw proper conclusions based on the results. This course is taught at a level assuming comfort with the course content in Mathematics (8011190), Statistics (8010848), and Econometrics (8011571).

Outline

• Cross-sectional Data Models for Discrete and Limited Dependent Variables

1. Binary outcomes models
2. Multinomial outcomes models
3. Models for truncated/censored data and sample selection models
4. Count data models

• Panel Data Models

1. ​Static linear panel data models: within group, first-difference, GLS and between group estimators
2. Dynamic linear panel data models
3. Nonlinear models: binary, truncated/censored and count outcomes
4. (Optional: Spatial panel data models)

• Treatment Evaluation

1. ​Setup and main assumptions
2. Treatment effects and selection bias
3. Matching and propensity score estimators
4. Differences-in-differences estimators
5. Regression discontinuity design

References and material

• Cameron A.C. and Trivedi P.K. (2005), Microeconometrics, Cambridge University Press, New York.
• Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
• Wooldridge J.M. (2010), Econometric Analysis of Cross-Section and Panel Data,  2nd ed., MIT Press, Cambridge (MA).

The material, including lecture slides and related Stata tutorials, will be posted on the course web site. The main textbook is Wooldridge (2010), but it may be complemented by some chapters from Cameron and Trivedi (2005) and Peracchi (2001).

Aggiornato A.A. 2017-2018

Overview and requirements

The course provides an overview of microeconometric methods for both cross-sectional and panel data and is structured so as to have a practical flavor, with a special emphasis on intuitions and applications. It combines standard lectures on microeconometric techniques together with tutorial lectures illustrating how the theory can be put to effective use through the Stata statistical software. It is especially suitable for students who want to address an empirical question in their master's thesis. After following the course, students should be able to understand when it is appropriate to apply a given method, the assumptions under which it is valid, and how it can be practically implemented.

The course is taught at a level that assumes comfort with the course content in Mathematics (8011190), Statistics (8010848), and Econometrics (8011571).

Outline

• Introduction and Background

1. ​Conditional expectations 
2. Partial effects

• Cross-sectional Data Models for Discrete and Limited Dependent Variables

1. Binary outcomes models
2. Multinomial outcomes models
3. Models for truncated/censored data and sample selection models
4. Count data models

• Panel Data Models

1. ​Static linear panel data models: within group, first-difference, GLS and between group estimators
2. Repeated cross-sections, heterogeneous panels, unbalanced panels and panel attrition
3. Dynamic linear panel data models
4. Nonlinear models: binary, truncated/censored and count outcomes

• Treatment Evaluation

1. ​Setup and main assumptions
2. Treatment effects and selection bias
3. Matching and propensity score estimators
4. Differences-in-differences estimators
5. Regression discontinuity design

References and material

• Cameron A.C. and Trivedi P.K. (2005), Microeconometrics, Cambridge University Press, New York.
• Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
• Wooldridge J.M. (2010), Econometric Analysis of Cross-Section and Panel Data,  2nd ed., MIT Press, Cambridge (MA).

The material, including lecture notes and related Stata tutorials, will be posted on the course web site. The main textbook is Wooldridge (2010), but it may be complemented by some chapters from Cameron and Trivedi (2005) and Peracchi (2001).

Aggiornato A.A. 2016-2017

Overview and requirements

The course provides an overview of microeconometric methods for both cross-sectional and panel data and is structured so as to have a practical flavor, with a special emphasis on intuitions and applications. It combines standard lectures on microeconometric techniques together with tutorial lectures illustrating how the theory can be put to effective use through the Stata statistical software. It is especially suitable for students who want to address an empirical question in their master's thesis. After following the course, students should be able to understand when it is appropriate to apply a given method, the assumptions under which it is valid, and how it can be practically implemented.

The course is taught at a level that assumes comfort with the course content in Mathematics (8011190), Statistics (8010848), and Econometrics (8011571).

Outline

• Introduction and Background

1. ​Conditional expectations 
2. Partial effects

• Cross-sectional Data Models for Discrete and Limited Dependent Variables

1. Binary outcomes models
2. Multinomial outcomes models
3. Models for truncated/censored data and sample selection models
4. Count data models

• Panel Data Models

1. ​Static linear panel data models: within group, first-difference, GLS and between group estimators
2. Repeated cross-sections, heterogeneous panels, unbalanced panels and panel attrition
3. Dynamic linear panel data models
4. Nonlinear models: binary, truncated/censored and count outcomes

• Treatment Evaluation

1. ​Setup and main assumptions
2. Treatment effects and selection bias
3. Matching and propensity score estimators
4. Differences-in-differences estimators
5. Regression discontinuity design

References and material

• Cameron A.C. and Trivedi P.K. (2005), Microeconometrics, Cambridge University Press, New York.
• Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
• Wooldridge J.M. (2010), Econometric Analysis of Cross-Section and Panel Data,  2nd ed., MIT Press, Cambridge (MA).

The material, including lecture notes and related Stata tutorials, will be posted on the course web site. The main textbook is Wooldridge (2010), but it may be complemented by some chapters from Cameron and Trivedi (2005) and Peracchi (2001).