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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
The multinomial probit (MNP) model offers a rather general and flexible framework for the analysis of discrete choices obtained from panel data and the specification of models with general error structures. However, this flexibility of specification has come at a relatively high price in terms of difficulty of computing maximum likelihood estimates ofthe model parameters and evaluating the associated choice function. This paper presents a new procedure for the estimation of MNP models, motivated primarily by the advances that have taken place in terms of computing environments and capabilities. the procedure derives its efficiency from execution in a parallel computing environment (CRAY YMP/8) and its accuracy from the use of better (but otherwise more computationally intensive) mathematical procedures. The paper also discusses and compares alternative nonlinear optimization procedures to search for the parameter values that maximize the likelihood function, in connection with Monte Carlo evaluation of the likelihood of each observation. The results are also compared to those obtained using the Clark approximation to evaluate the choice probabilities.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
The multinomial probit (MNP) model offers a rather general and flexible framework for the analysis of discrete choices obtained from panel data and the specification of models with general error structures. However, this flexibility of specification has come at a relatively high price in terms of difficulty of computing maximum likelihood estimates ofthe model parameters and evaluating the associated choice function. This paper presents a new procedure for the estimation of MNP models, motivated primarily by the advances that have taken place in terms of computing environments and capabilities. the procedure derives its efficiency from execution in a parallel computing environment (CRAY YMP/8) and its accuracy from the use of better (but otherwise more computationally intensive) mathematical procedures. The paper also discusses and compares alternative nonlinear optimization procedures to search for the parameter values that maximize the likelihood function, in connection with Monte Carlo evaluation of the likelihood of each observation. The results are also compared to those obtained using the Clark approximation to evaluate the choice probabilities.
Author: Carlos Daganzo
Publisher: Elsevier
ISBN: 1483299341
Category : Business & Economics
Languages : en
Pages : 239
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Book Description
Multinomial Probit
Author: David S. Bunch
Publisher:
ISBN:
Category : Automobile ownership
Languages : en
Pages : 110
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Book Description
Author: Yosef Sheffi
Publisher:
ISBN:
Category : Probits
Languages : en
Pages : 28
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Book Description
Author: Nicholas M. Kiefer
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 48
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Book Description
Author: Vani K. Borooah
Publisher: SAGE
ISBN: 9780761922421
Category : Mathematics
Languages : en
Pages : 108
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Book Description
Many problems in the social sciences are amenable to analysis using the analytical tools of logit and probit models. This book explains what ordered and multinomial models are and also shows how to apply them to analysing issues in the social sciences.
Author: Vani K. Borooah
Publisher: SAGE
ISBN: 9780761922421
Category : Mathematics
Languages : en
Pages : 108
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Book Description
Many problems in the social sciences are amenable to analysis using the analytical tools of logit and probit models. This book explains what ordered and multinomial models are and also shows how to apply them to analysing issues in the social sciences.
Author: Priyadarshan Nandkumar Patil
Publisher:
ISBN:
Category :
Languages : en
Pages : 70
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Book Description
A simulation evaluation is presented to compare alternative estimation techniques for a five-alternative multinomial probit (MNP) model with random parameters, including cross-sectional and panel datasets and for scenarios with and without correlation among random parameters. The different estimation techniques assessed are: (1) The maximum approximate composite marginal likelihood (MACML) approach; (2) The Geweke-Hajivassiliou-Keane (GHK) simulator with Halton sequences, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; (3) The GHK approach with sparse grid nodes and weights, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; and (4) a Bayesian Markov Chain Monte Carlo (MCMC) approach. In addition, for comparison purposes, the GHK simulator with Halton sequences was implemented in conjunction with the traditional, full information maximum likelihood approach as well. The results indicate that the MACML approach provided the best performance in terms of the accuracy and precision of parameter recovery and estimation time for all data generation settings considered in this study. For panel data settings, the GHK approach with Halton sequences, when combined with the CML approach, provided better performance than when implemented with the full information maximum likelihood approach, albeit not better than the MACML approach. The sparse grid approach did not perform well in recovering the parameters as the dimension of integration increased, particularly so with the panel datasets. The Bayesian MCMC approach performed well in datasets without correlations among random parameters, but exhibited limitations in datasets with correlated parameters.
Author: Ranjini Natarajan
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 256
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Book Description
Author: Roman Liesenfeld
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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]
