An efficient decomposition of the expectation of the maximum for the multivariate normal and related distributions

Jonathan Eggleston ()

Journal of Econometrics, 2016, vol. 195, issue 1, 120-133

Abstract: In structural dynamic discrete choice models, Monte Carlo integration has been the only way to evaluate the expectation of the maximum when errors are normally distributed. In this paper, however, I show that the expectation of the maximum can be decomposed as a linear combination of multivariate normal CDFs. For related distributions, such as the multivariate t-distribution, this expectation has a similar decomposition. My computational results show speed benefits of my proposed method for models with a low number of choices, although the speed gains are contingent on the use of analytical derivatives as opposed to numerical derivatives.

Keywords: Expectation of the maximum; Emax; Multivariate normal; Monte Carlo integration; Dynamic structural models (search for similar items in EconPapers)
JEL-codes: C25 C61 C63 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407616301361
Full text for ScienceDirect subscribers only

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:eee:econom:v:195:y:2016:i:1:p:120-133

DOI: 10.1016/j.jeconom.2016.07.003

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().