Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: http://hdl.handle.net/10419/22042
Autoren: 
Liesenfeld, Roman
Richard, Jean-François
Datum: 
2007
Schriftenreihe/Nr.: 
Economics Working Paper No. 2007-26
Zusammenfassung: 
In this paper we discuss parameter identification and likelihood evaluation for multinomial multiperiod Probit models. It is shown in particular that the standard autoregressive specification used in the literature can be interpreted as a latent common factor model. However, this specification is not invariant with respect to the selection of the baseline category. Hence, we propose an alternative specification which is invariant with respect to such a selection and identifies coefficients characterizing the stationary covariance matrix which are not identified in the standard approach. For likelihood evaluation requiring high-dimensional truncated integration we propose to use a generic procedure known as Efficient Importance Sampling (EIS). A special case of our proposed EIS algorithm is the standard GHK probability simulator. To illustrate the relative performance of both procedures we perform a set Monte-Carlo experiments. Our results indicate substantial numerical e?ciency gains of the ML estimates based on GHK-EIS relative to ML estimates obtained by using GHK.
Schlagwörter: 
Discrete choice
Importance sampling
Monte-Carlo integration
Panel data
Parameter identification
Simulated maximum likelihood
JEL: 
C35
C15
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
610.63 kB





Publikationen in EconStor sind urheberrechtlich geschützt.