Robust Tests of Model Incompleteness in the Presence of Nuisance Parameters

(With Shuowen Chen)

We develop a novel test of model incompleteness and analyze its asymptotic properties. A key observation is that one can define a least-favorable parametric model along which detecting local alternatives without knowing the selection mechanism is the hardest. The test builds on a score function constructed from such a model. The proposed procedure remains computationally tractable even in the presence of nuisance parameters because it suffices to estimate them only under the null hypothesis of model completeness.

We illustrate the test by applying it to a market entry model and a triangular model with a set-valued control function.

Registration

You can sign up for this seminar by sending an email to eb-secr@ese.eur.nl. The lunch will be provided (vegetarian option included).

Coordinators

• Maria Grith

• Mikhail Zhelonkin

More information

Do you want to know more about the event, contact the secretariat Econometrics at eb-secr@ese.eur.nl.