The Multinomial Multiperiod Probit Model: Identification and Efficient Estimation

Roman Liesenfeld and Jean-Francois Richard

No 2007-26, Economics Working Papers from Christian-Albrechts-University of Kiel, Department of Economics

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 (search for similar items in EconPapers)
JEL-codes: C15 C35 (search for similar items in EconPapers)
Date: 2007
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cauewp:6340

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