The Multinomial Multiperiod Probit Model: Identification and Efficient Estimation

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Economics Working Paper ; No. 2007-26

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions ; Statistical Simulation Methods: General
• Subject: Discrete choice ; Importance sampling ; Monte-Carlo integration ; Panel data ; Parameter identification ; Simulated maximum likelihood ; Probit-Modell ; Schätztheorie ; Maximum-Likelihood-Methode ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Liesenfeld, Roman ; Richard, Jean-François
• Event: Veröffentlichung
• (who): Kiel University, Department of Economics
• (where): Kiel
• (when): 2007

Further information

• Handle: http://hdl.handle.net/10419/22042
• Last update: 10.03.2025, 11:43 AM CET