Generalized Maximum Entropy Estimation of Dynamic Programming Models with Sample Selection Bias

Rosa Papalia

Computational Statistics, 2003, vol. 18, issue 3, 463-475

Abstract: In this study, an alternative estimation strategy is suggested, which requires the solution of a dynamic programming problem, expressed in term 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. Copyright Physica-Verlag 2003

Keywords: Dynamic Programming Models; Corner solutions; Generalized Maximum Entropy; Optimization Problem (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/BF03354610 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:18:y:2003:i:3:p:463-475

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/BF03354610

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().