Generalized maximum entropy estimation of dynamic programming models with sample selection bias

Generalized maximum entropy estimation of dynamic programming models with sample selection bias

0.00 Avg rating0 Votes
Article ID: iaor20042294
Country: Germany
Volume: 18
Issue: 3
Start Page Number: 463
End Page Number: 475
Publication Date: Jul 2003
Journal: Computational Statistics
Authors:
Keywords: entropy
Abstract:

In this study, an alternative estimation strategy is suggested, which requires the solution of a dynamic programming problem, expressed in terms of conditional choice value functions. The problem of defining the Euler equation for the corner solution case is overcome by introducing an additional constraint on the conditional choice value functions, which provides a correction for the sample selection bias. The problem of estimating the parameters of the resulting objective function and of the state equation is solved using numerical methods in a linear-quadratic inverse control problem which consists of an objective function together with the set of introduced constraints. First step estimates are computed by generalized maximum entropy estimation techniques.

Reviews

Required fields are marked *. Your email address will not be published.