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 2020/2021

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

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.

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.

Teaching tools

Course website with news and updated materials, available on the course webpage accessible trhough the link below (see Link to further information).
Lectures slides, made available on the same webpage. Software STATA: available for students of the Department of Economics (CAMPUS license) and at the Computer Lab of the School of Economics and Management.

Links to further information


Office hours

See the website of Chiara Monfardini