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Author: Roman Liesenfeld
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Languages : en
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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 efficiency gains of the ML estimates based on GHK-EIS relative to ML estimates obtained by using GHK.
Author: Roman Liesenfeld
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ISBN:
Category :
Languages : en
Pages :
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Book Description
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 efficiency gains of the ML estimates based on GHK-EIS relative to ML estimates obtained by using GHK.
Author: John F. Geweke
Publisher:
ISBN:
Category :
Languages : en
Pages : 34
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Author: Roman Liesenfeld
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Category :
Languages : en
Pages :
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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." [author's abstract]
Author: Andreas Ziegler
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Nicholas M. Kiefer
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Category : Estimation theory
Languages : en
Pages : 48
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Category :
Languages : en
Pages : 27
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Author: Carlos Daganzo
Publisher: Elsevier
ISBN: 1483299341
Category : Business & Economics
Languages : en
Pages : 239
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Book Description
Multinomial Probit
Author: Seong Yong Park
Publisher:
ISBN:
Category : Consumers' preferences
Languages : en
Pages : 258
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Author: Axel Boersch-Supan
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Category :
Languages : es
Pages : 27
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Author: Andreas Ziegler
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Languages : en
Pages : 0
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Within the framework of Monte Carlo experiments, this paper systematically compares different versions of the simulated z-test (using the GHK simulator) in one- and multiperiod multinomial probit models. One important finding is that, in the flexible probit models, the tests on parameters of explanatory variables mostly provide robust results in contrast to the tests on variance-covariance parameters. Overall, neither the amount of random draws in the GHK simulator nor the choice of a certain version of the simulated z-test have a strong influence on the test results. This finding refers to the conformity between the shares of type I errors and the basic significance levels as well as to the number of type II errors. In contrast, the number of type II errors in the simulated z-tests on variance-covariance parameters is reduced by increasing the sample size. Effects of misspecifications on simulated z-tests only appear in the multiperiod multinomial probit model. In this case, the inclusion of the concept of the quasi maximum likelihood theory in the simulated z-test provides comparatively more favourable results.
