Sequential and Full Information Maximum Likelihood Estimation of a Nested Logit Model
Sequential estimation of a nested logit model is in general not an empirically desirable procedure, either as an alternative to full information maximum likelihood (FIML) estimation or as a source of param eter starting values for FIML estimation. Empirical studies which use varying ch oice sets across the sampled population create ambiguity in the link between seq uential and simultaneously estimated nested logit models. FIML estimation is now a computationally feasible strategy as illustrated herein. Copyright 1986 by MIT Press.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 68 (1986)
Issue (Month): 4 (November)
|Contact details of provider:|| Web page: http://mitpress.mit.edu/journals/|
|Order Information:||Web: http://mitpress.mit.edu/journal-home.tcl?issn=00346535|
When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:68:y:1986:i:4:p:657-67. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anna Pollock-Nelson)
If references are entirely missing, you can add them using this form.