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The dynamic invariant multinomial probit model: Identification, pretesting and estimation

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  • Liesenfeld, Roman
  • Richard, Jean-François

Abstract

We present a new specification for the multinomial multiperiod probit model with autocorrelated errors. In sharp contrast with commonly used specifications, ours is invariant with respect to the choice of a baseline alternative for utility differencing. It also nests these standard models as special cases, allowing for data-based selection of the baseline alternatives for the latter. Likelihood evaluation is achieved under an Efficient Importance Sampling (EIS) version of the standard GHK algorithm. Several simulation experiments highlight identification, estimation and pretesting within the new class of multinomial multiperiod probit models.

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  • Liesenfeld, Roman & Richard, Jean-François, 2010. "The dynamic invariant multinomial probit model: Identification, pretesting and estimation," Journal of Econometrics, Elsevier, vol. 155(2), pages 117-127, April.
    • Handle: RePEc:eee:econom:v:155:y:2010:i:2:p:117-127
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    Cited by:

    1. Ziegler Andreas, 2010. "Z-Tests in Multinomial Probit Models under Simulated Maximum Likelihood Estimation: Some Small Sample Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(5), pages 630-652, October.

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