CE26 - Innovation, travail

Moment Conditions Models and Bayesian Inference for Policy Evaluation – MomBay

Submission summary

Economic models for policy evaluation and labor markets often imply restrictions on observable and unobservable quantities, and on a structural parameter that are written in the form of moment conditions (MCs). The structural parameter of the MC model has a causal interpretation and the social planner wants to know its value in order to decide the policy to undertake. This project develops Bayesian causal inference and prediction for this type of conditional and unconditional MC models by making minimal assumptions on the data distribution. Our procedure is based on the Exponential Tilted Empirical Likelihood and we will show it is valid for both Bayesian and frequentist inference. Estimating causal effects is important in socio-economic situations of scarce resources in order to know the best treatment that has to be administrated to achieve a given goal. In addition to theoretical econometric tools we will provide the computational tools to easily implement our procedure.

Project coordination

Anna Simoni (Centre de Recherche en Economie et Stastistique - CREST)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

CREST Centre de Recherche en Economie et Stastistique - CREST

Help of the ANR 150,080 euros
Beginning and duration of the scientific project: November 2021 - 48 Months

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