Discrete choice theory, information theory and the multinomial logit and gravity models

Anas, Alex

doi:10.1016/0191-2615(83)90023-1

Cited by 289 publications

(143 citation statements)

References 4 publications

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“…Various options are available, but we choose to use a doubly-constrained gravity model. The approach is popular and has been shown by Anas (1983) to give identical results to the multinomial logit model. This means it is possible to give a random utility interpretation to the parameters in the doublyconstrained gravity model.…”

Section: The Commuting Modelmentioning

confidence: 93%

The suitability of hedonic models for cost-benefit analysis: Evidence from commuting flows

Gjestland¹,

McArthur

Osland³

et al. 2014

Transportation Research Part A: Policy and Practice

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Section: The Commuting Modelmentioning

confidence: 93%

The suitability of hedonic models for cost-benefit analysis: Evidence from commuting flows

Gjestland¹,

McArthur

Osland³

et al. 2014

Transportation Research Part A: Policy and Practice

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“…As mentioned in the introduction, it is possible to derive the gravity model from the multinomial logit model of discrete choice (Anas, 1983;Anas, 1975;Williams, 1977). Because of this, the trip distribution matrix can be viewed as an aggregation of individual choices which are themselves the result of draws from a given distribution.…”

Section: A New Testing Proceduresmentioning

confidence: 99%

The Spatial Transferability of Parameters in a Gravity Model of Commuting Flows

McArthur

Kleppe²,

Thorsen³

et al. 2010

SSRN Journal

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This paper studies whether gravity model parameters estimated in one geographic area can give reasonable predictions of commuting flows in another. To do this, three sets of parameters are estimated for geographically proximate yet separate regions in south-west Norway. All possible combinations of data and parameters are considered, giving a total of nine cases. Of particular importance is the distinction between statistical equality of parameters and 'practical' equality i.e. are the differences in predictions big enough to matter. A new type test based on the Standardised Root Mean Square Error (SRMSE) and Monte Carlo simulation is proposed and utilised.

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“…Recently, it has been shown (Nijkamp and Reggiani, 1986) that discrete choice models can consistently be interpreted in the context of spatial interaction models and Alonso's general theory of movement, in both a static and a dynamic sense: (dynamic) discrete choice models have strong roots in (dynamic) generalized spatial interaction analysis (see also Anas, 1983).…”

Section: Outlook: Alternative Approaches To Modelling the Dynamics Ofmentioning

confidence: 99%

From static towards dynamic discrete choice modelling

Fischer

Nijkamp

1987

Regional Science and Urban Economics

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The main aim of the present paper is to survey sorae major trends in current research in the field of discrete choice modelling, with particular emphasis on dynamic approaches. The paper is organized as follows. Section 2 provides a brief overview of static disaggregate choice modelling and random utility maximimation, based inter alia on multinomial logit and/or probit models, generalized extreme value models, and nested logit models. Particular attention is given here to model representation issues, sampling and estimation issues and model performance issues. Next, section 3 is devoted to some recent developments in the rapidly growing new field of dynamic discrete choice modelling. In contrast to stochastic panel data models of buying behaviour, dynamic discrete choice models incorporate explanatory variables and take adaptive behaviour explicitly into account (i.e. the effect of past experience on choice behaviour). Several dynamic discrete choice model approaches are summarized. Special attention is paid to the seminal work of Heekman. In the final section, complementary and alternative approaches to dynamic choice modelling are discussed, such as the human activity constraint approach, the computational process modelling approach and the master equation approach. It is concluded that contextual effects, multi-actor or synergetic interactions and shifting individual preferences based-on learning principles are of primary importance in dynamic discrete choice modelling.

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Discrete choice theory, information theory and the multinomial logit and gravity models

Cited by 289 publications

References 4 publications

The suitability of hedonic models for cost-benefit analysis: Evidence from commuting flows

The suitability of hedonic models for cost-benefit analysis: Evidence from commuting flows

The Spatial Transferability of Parameters in a Gravity Model of Commuting Flows

From static towards dynamic discrete choice modelling

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