Some Remarks on CCP-based Estimators of Dynamic Models
17 Pages Posted: 22 Mar 2021 Last revised: 20 Aug 2021
Date Written: May 9, 2021
This note provides several remarks relating to the conditional choice probability (CCP) based estimation approaches for dynamic discrete-choice models. Specifically, the Arcidiacono and Miller  estimation procedure relies on the “inverse-CCP” mapping from CCP’s to choice-specific value functions. Exploiting the convex-analytic structure of discrete choice models, we discuss two approaches for computing this, using either linear or convex programming, for models where the utility shocks can follow arbitrary parametric distributions. Furthermore, the inverse-CCP mapping is generally distinct from the “selection adjustment” term (i.e. the expectation of the utility shock for the chosen alternative), so that computational approaches for computing the latter may not be appropriate for computing inverse-CCP mapping.
Keywords: dynamic discrete choice, random utility, linear programming, convex analysis, convex optimization
JEL Classification: C35, C61, D90
Suggested Citation: Suggested Citation