| By: |
Liesenfeld, Roman;
Richard, Jean-Francois |
| Abstract: |
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. |
| Keywords: |
Discrete choice, Importance sampling, Monte-Carlo integration, Panel data, Parameter identification, Simulated maximum likelihood |
| JEL: |
C15 C35 |
| Date: |
2007 |
| URL: |
http://d.repec.org/n?u=RePEc:zbw:cauewp:6340&r=dcm |