Alternative Simulation-Based Estimators of Logit Models with Random Effects

Giorgio Calzolari, Fabrizia Mealli () and Carla Rampichini

Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti"

Abstract: Logit models with random effects are now widely used in applied Statistics and Econometrics. They usually lead to intractable likelihood functions, as they involve integrals without closed form solution. Numerical integration can be used to compute the likelihood and software is available (Hedeker and Gibbons, 1996). Difficulties can be encountered when the number of random effect parameters is not very small. With a detailed Monte Carlo experimentation, we show in this paper that the simulation-based estimators are almost as efficient as maximum likelihood. They are Simulated Maximum Likelihood (Gouri´eroux and Monfort, 1991), Indirect Inference (Gouri´eroux, Monfort and Renault, 1993) using an auxiliary approximated likelihood estimator, and Indirect Inference using an auxiliary linear probability model. The advantage of the latter is its great simplicity and computational speed.

Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://www.ds.unifi.it/ricerca/pubblicazioni/quade ... i_doc/quaderno48.pdf (application/pdf)

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: http://EconPapers.repec.org/RePEc:fir:econom:quaderno48

Access Statistics for this paper

More papers in Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti"
Address: Viale G.B. Morgagni, 59 - I-50134 Firenze - Italy
Contact information at EDIRC.
Series data maintained by Margherita Velucchi ().