90351 - Advanced Microeconometrics

Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Gender equality Decent work and economic growth

Academic Year 2021/2022

Learning outcomes

At the end the course the student will have understood the potential of simulation based approaches to solve inference problems arising in various microeconometric models, including models for simultaneous choices (multivariate models) and models for the choice among many alternatives (multinomial models). In particular, she/he will be able: - to critically understand the applications of these models in the recent empirical economic literature; - to implement selected simulation based estimation techniques by way of specific routines, using the STATA software.

Course contents

The course requires the contents of Econometrics 2 (Master in Economics) as prerequisite knowledge.

1. Introduction to simulation based estimation methods and motivation

2. Review of Maximum Likelihood. Limited dependent variable models whose generalization requires simulation based inference:

  • Sample selection model
  • Poisson model
  • Bivariate/multivariate probit nodel
  • Multinomial logit model

3. Simulation preliminaries

  • Integration by simulation
  • drawing from densities

4. Method of simulated maximum likelihood

5. Discrete choice with simulation

  • Multinomial mixed-logit and probit mdoels
  • Static and dynamic binary choice models for panel data
  • Multinomial and multivariate discrete choice models


Cameron, A.C., Trivedi, P.K. (2005) "Microeconometrics", Cameron, A.C., Cambridge University Press

Cameron, A.C., (2009) "Microeconometrics Using STATA", Stata Press

Gourieroux, C.; Monfort, ( A. "Simulation-Based Econometric Methods", Oxford University Press, 1996

Train, K. E. (2003, 2009), "Discrete Choice Methods with Simulation", Cambridge University Press ,

Verbeek, M. (2017), "A Guide to Modern Econometrics", Wiley Custom

Wooldridge, J.M. (2013), "Econometric Analysis of Cross Section and Panel Data", The MIT Press

Further references to published papers will be provided during the course

Teaching methods

Throughout the course, the presentation of theoretical issues will be complemented by critical discussion of some applications from recent applied microeconometrics research. Students will learn how to apply the various methods/models to real data using the software STATA.

During the lectures, the presentation of theoretical issues will be complemented by critical discussion of some applications from recent applied microeconometrics research.

Students will receive data to practice at the computer and learn how to apply the various models using the software STATA, which is available to them through the CAMPUS license.

Students will receive take home problem sets, to be solved in small groups and handed in with specific deadlines. These homework require data analysis work and writing short essays.

Assessment methods

Home assignment, individual or in groups of 2 or 3 people, to be presented and discussed at the esam date. The assignment will involve the critical summary of an applied paper using simulation based methods and a new real data application of the methods performed by the students using the software STATA.

In case online exams will be envisaged by the University of Bologna, the structure of written exam is the same. The discussion will be run through Zoom

The maximum possible score is 30 cum laude, in case all anwers are correct, complete and formally rigorous.

The grade is graduated as follows:

<18 failed
18-23 sufficient
24-27 good
28-30 very good
30 e lode excellent

Teaching tools

Dedicated page on the VIRTUALE platform containing:

  • News and updated information
  • Lectures slides
  • STATA lab material

Software STATA: can be installed on students' personal computers (CAMPUS license) and is available at the Computer Lab of the School of Economics and Management.

Office hours

See the website of Chiara Monfardini