This seminar features research on theoretical and applied econometrics. It is held the second Monday of each month.
- Scientific contact: Philipp Ketz
- Logistic contact: Sophie Gozlan
This seminar is co-funded by a French government subsidy managed by the Agence Nationale de la Recherche under the framework of the Investissements d’avenir programme reference ANR-17-EURE-0001.
- Monday 14 December 2020 16:00-17:15
- FREYBERGER Joachim (University of Bonn) : TBA
- Monday 8 March 2021 16:00-17:15
- KASY Maximilian (University of Oxford) : TBA
- Monday 12 April 2021 16:00-17:15
- KOCK Anders (Aarhus University/University of Oxford) : TBA
- Monday 10 May 2021 16:00-17:15
- ABADIE Alberto (Harvard University ) : TBA
- Monday 14 June 2021 16:00-17:15
- KOOPMAN Siem Jan ( Vrije Universiteit Amsterdam) : TBA
- Monday 9 November 2020 16:00-17:15
- RENAULT Eric (University of Warwick) : Approximate Maximum Likelihood for Complex Structural Models
- Co-authors: D.T. Frazier and V. Czellar
- AbstractIndirect Inference (I-I) is a popular technique for estimating complex parametric models whose likelihood function is intractable, however, the statistical efficiency of I-I estimation is questionable. While the efficient method of moments, Gallant and Tauchen (1996), promises efficiency, the price to pay for this efficiency is a loss of parsimony and thereby a potential lack of robustness to model misspecification. This stands in contrast to simpler I-I estimation strategies, which are known to display less sensitivity to model misspecification precisely due to their focus on specific elements of the underlying structural model. In this research, we propose a new simulation-based approach that maintains the parsimony of I-I estimation, which is often critical in empirical applications, but can also deliver estimators that are nearly as efficient as maximum likelihood. This new approach is based on using a constrained approximation to the structural model, which ensures identification and can deliver estimators that are nearly efficient. We demonstrate this approach through several examples, and show that this approach can deliver estimators that are nearly as efficient as maximum likelihood, when feasible, but can be employed in many situations where maximum likelihood is infeasible.
- Full text [pdf]
- Monday 12 October 2020 16:00-17:15
- on line
- GUNSILIUS Florian (University of Michigan) : Distributional synthetic controls
- AbstractThis article extends the method of synthetic controls to probability measures. The distribution of the synthetic control group is obtained as the optimally weighted barycenter in Wasserstein space of the distributions of the control groups which minimizes the distance to the distribution of the treatment group. It can be applied to settings with disaggregated- or aggregated (functional) data. The method produces a generically unique counterfactual distribution when the data are continuously distributed. A basic representation of the barycenter provides a computationally efficient implementation via a straightforward tensor-variate regression approach. In addition, identification results are provided that also shed new light on the classical synthetic controls estimator. As an illustration, the method provides an estimate of the counterfactual distribution of household income in Colorado one year after Amendment 64.
- Full text [pdf]
- Monday 14 September 2020 16:00-17:15
- KAMAT Vishal (Toulouse School of Economics) : Estimating the Welfare Effects of School Vouchers
- Co-author: S. Norris
- AbstractWe analyze the welfare effects of voucher provision in the DC Opportunity Scholarship Program (OSP), a school voucher program in Washington, DC, that randomly allocated vouchers to students. To do so, we develop new discrete choice tools to show how to use data with random allocation of school vouchers to characterize what we can learn about the welfare benefits of providing a voucher of a given amount, as measured by the average willingness to pay for that voucher, and these benefits net of the costs of providing that voucher. A novel feature of our tools is that they allow specifying the relationship of the demand for the various schools with respect to prices to be entirely nonparametric or to be parameterized in a flexible manner, both of which do not necessarily imply that the welfare parameters are point identified. Applying our tools to the OSP data, we find that provision of the status-quo as well as a wide range of counterfactual voucher amounts has a positive net average benefit. We find these positive results arise due to the presence of many low-tuition schools in the program, removing these schools from the program can result in a negative net average benefit.
- Full text [pdf]