This course will provide participants with the essential tools, both theoretical and applied, for a proper use of modern micro-econometric methods for policy evaluation and causal counterfactual modelling under both assumptions of “selection on observables” and “selection on unobservables”. The course will cover these approaches: Regression adjustment (parametric and nonparametric), Matching (on covariates and on propensity score), Reweighting and Double-robust methods, and Difference-in-differences methods.
After attending the course, the participant will be able to set up and manage a correct evaluation design under observable and unobservable selection on his own: identification of the policy framework, collection and management of suitable datasets, use of appropriate econometric methods, and interpretation of results. Potential applications are in different contexts of policy such as: finance and banking, the labour market, the investment activities of enterprises, education policy and regional cooperation, incentives for business research and development, etc. Although they can be used in any further field of study aiming at estimating the ex-post impact of a given policy intervention on specific targets. The course will provide various instructional examples on real datasets.
||Q&A with Instructor
Session 1: Introduction to the Econometrics of Program Evaluation
- Econometrics of program evaluation: an overview
- Experimental and non-experimental design
- The selection problem: observable and unobservable selection
- Assumptions and notation
- Treatment effect estimation and counterfactual causality
- The Stata teffects package and related Stata commands
Session 2: Regression Adjustment
- Working under selection on observables
- Linear and nonlinear Regression Adjustment
- The Stata command teffects ra and ivtreatreg
- Stata applications using real datasets
Session 1:Matching and Reweighting
- Matching estimator: an introduction
- Covariate Matching versus propensity-score Matching
- The Reweighting and the Double-robust estimator
- The Stata commands teffects psmatch, teffects nnmatch, and teffects ipw
- Stata applications using real datasets
- Difference-in-differences (DID) for program evaluation
- Relaxing the observable selection assumption
- DID with longitudinal data
- DID with repeated cross-section
- DID analysis for pre- and post-treatment effects
- Applications using Stata
Principal texts for pre/post course reading
See references here:
- Wooldridge, J.M. (2010). Econometric Analysis of cross section and panel data. Chapter 21. Cambridge: MIT Press.
- Cameron, A.C., & Trivedi P.K. (2005). Microeconometrics: Methods and Applications. Chapter 25. Cambridge: Cambridge University Press.
- Cerulli, G. (2012), An Assessment of the Econometric Methods for Program Evaluation and a Proposal to Extend the Difference-In-Differences estimator to dynamic treatment, in: Econometrics: New Developments, Nova Publishers, New York.
Learning Ratio 30% Theory, 30% Demonstration and 40% Practical
Knowledge of basic econometrics: notion of conditional expectation and related properties; point and interval estimation; regression model and related properties; probit and logit regression.
Basic knowledge of the Stata software
Terms & Conditions
- Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
- Additional discounts are available for multiple registrations.
- Delegates are provided with temporary licences for the principal software package(s) used in the delivery of the course. It is essential that these temporary training licenses are installed on your computers prior to the start of the course.
- Payment of course fees required prior to the course start date.
- Registration closes 1 calendar day prior to the start of the course.
- 100% fee returned for cancellations made more than 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
The number of attendees is restricted. Please register early to guarantee your place.