Linear Demand Systems are Inconsistent with Discrete Choice

2010 ◽  
Vol 10 (1) ◽  
Author(s):  
Sonia Jaffe ◽  
E. Glen Weyl
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Model ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Second Order ◽  
Demand Systems ◽  
Linear Demand

We show that with more than two options, a discrete choice model cannot generate linear demand. In doing so, we demonstrate a prediction of such discrete choice models that is falsifiable based on local second-order properties of demand.

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.

Unobserved Heterogeneity in Dynamic Discrete Choice Models

1993 ◽  
Vol 25 (4) ◽  
pp. 495-519 ◽  
Author(s):  
S Reader
Keyword(s):  
Discrete Choice ◽  
Simulation Analysis ◽  
Choice Model ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Estimation Procedure ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Dynamic Discrete Choice ◽  
Cross Sectional

Monte Carlo simulation methods are used to confirm the identifiability of discrete choice models in which unobserved heterogeneity is specified as a random effect and modelled using the nonparametric mass-points approach. This simulation analysis is also used to examine alternative strategies for the estimation of such models by using a quasi-Newton maximum-likelihood estimation procedure, given the apparent sensitivity of model identification to choice of starting values. A mass-point model approach is then applied to a dataset of repeated choice involving household shopping trips between three types of retail centre, and the results from this approach are compared with those obtained from a conventional cross-sectional multinomial logit choice model as well as to results from a model in which a parametric distribution (the Dirichlet) is used to model the unobserved heterogeneity.

A simultaneous decision-making approach to model the demand for freight transportation

1991 ◽  
Vol 18 (3) ◽  
pp. 515-520 ◽  
Author(s):  
W. M. Abdelwahab ◽  
M. A. Sargious
Keyword(s):  
Maximum Likelihood ◽  
Discrete Choice ◽  
Choice Model ◽  
Mode Choice ◽  
Freight Transportation ◽  
Choice Models ◽  
Ordinary Least Squares ◽  
Discrete Choice Models ◽  
Estimation Methods ◽  
Complete Model

The application of discrete choice models (e.g., logit, probit) to study modal choice in passenger transportation has had a wide acceptance in the literature. However, little success had been reported on the application of these models to study the demand for freight transportation. This is mainly because in freight transportation a model that merely attempts to explain the choice of mode without taking into consideration other related factors, such as shipment size, is only one part of a complete model. Another type of models known as inventory-based models, which takes these factors into consideration, has been developed and applied with a greater success. However, the data requirement of these inventory models has hampered their applicability, especially in situations with limited data on goods movement. This paper presents a new approach to study the demand for intercity freight transportation. The model proposed in this paper utilizes the strength of discrete choice models (e.g., probit) in explaining the process of mode choice as one part of a complete model. The complete model is presented as a joint discrete/continuous choice model for the choices of mode and shipment size. The model is practical in that it requires the same amount and quality of data that would be required to develop a standard disaggregate mode choice model, and it can be estimated using simple two-stage estimation methods which utilizes standard probit maximum likelihood and ordinary least squares estimation techniques. Key words: disaggregate, freight transportation, maximum likelihood, mode, model, probit, shipment.

scholarly journals Modeling enterprise location choice decision behavior

2021 ◽  
Vol 14 (1) ◽  
pp. 669-691
Author(s):  
Nguyen Cao Y
Keyword(s):  
Spatial Correlation ◽  
Discrete Choice ◽  
Choice Model ◽  
Location Choice ◽  
Mixed Logit ◽  
Multinomial Logit ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Spatial Effects ◽  
Modeling Framework

This study presents a location choice model that incorporates urban spatial effects for enterprises. A modeling framework is developed to analyze decisions regarding location choice for enterprises using a series of discrete choice models including multinomial logit without any urban spatial effects, multinomial logit incorporating urban spatial effects, and mixed logit incorporating urban spatial effects. In this framework, urban spatial effects, such as the urban spatial correlation among enterprises in deterministic terms and the urban spatial correlation among zones in the error term, are captured by mixed logit models in particular and discrete choice models in general. The results indicate that the urban spatial effects and the land prices in a given zone strongly affect the decision-making process of all the enterprises in the Tokyo metropolitan area. Moreover, the important role of urban spatial effects in the proposed model will be clarification through comparing the three above models. This comparison will be implemented on the basis of three types of indicators such as the log likelihood ratio, Akaike information indicator, and hit ratio of each model.

Benchmarks for Discrete Choice Models

1994 ◽  
Vol 31 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Manohar U. Kalwani ◽  
Robert J. Meyer ◽  
Donald G. Morrison
Keyword(s):  
Discrete Choice ◽  
Consumer Choice ◽  
Repeated Measures ◽  
Choice Model ◽  
Simulated Data ◽  
Brand Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Choice Data ◽  
Choice Probabilities

In assessing the performance of a choice model, we have to answer the question, “Compared with what?” Analyses of consumer brand choice data historically have measured fit by comparing a model's performance with that of a naive model that assumes a household's choice probability on each occasion equals the aggregate market share of each brand. The authors suggest that this benchmark could form an overly naive point of reference in assessing the fit of a choice model calibrated on scanner-panel data, or any repeated-measures analysis of choice. They propose that fairer benchmarks for discrete choice models in marketing should incorporate heterogeneity in consumer choice probabilities, evidence for which is by now well documented in the marketing literature. They use simulated data to compare the performance of parametric and nonparametric benchmark models, which allow for heterogeneity in consumer choice probabilities, with the performance of the aggregate share-based benchmark model, which assumes consumers are homogeneous in their choice probabilities. They also assess the performance of two previously published consumer behavior models against the proposed fairer benchmark models that allow for heterogeneity in consumer choice probabilities. They find that one provides a significantly better fit than their more conservative benchmark models and the other performs less favorably.

Modeling Store Choices with Cross-Sectional and Pooled Cross-Sectional Data: A Comparison

1995 ◽  
Vol 27 (8) ◽  
pp. 1303-1315 ◽  
Author(s):  
J-C Thill
Keyword(s):  
Market Share ◽  
Discrete Choice ◽  
Marginal Utility ◽  
Choice Model ◽  
Practical Implication ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Destination Choice ◽  
Cross Sectional ◽  
Data Set

Contrary to many other types of spatial decisions, shopping destination choice behavior is highly repetitive. For the practitioner looking for good predictors of store patronage, for reliable marginal utility estimates and reliable market share predictions, a central concern is with the type of data best suited to the research question, given the existing logistic and financial constraints. Different approaches can be recognized in the literature in which conventional discrete choice models are applied to shopping destination choice problems. In this paper, two of the most common practices are assessed and compared. First, the choice model is estimated with all choices of a relevant destination observed during a certain period of time (pooled cross-sectional data). The alternative approach consists in an estimation with the choice of the destination where the majority of purchases takes place (cross-sectional data). In the particular data set employed here, no evidence is found to support the idea that a multinomial logit model estimated with cross-sectional data does not perform as well as a model estimated with pooled cross-sectional data. Both models are found to be similar in their ability to identity the main predictors of store choice. Models developed on either data sets have marginal utility estimates that exhibit no statistically significant differences. Finally, market share predictions derived from both models are not statistically different. It appears, therefore, that there is no need to collect repeated patronage data over an extended period of time. The practitioner who wishes to use a conventional discrete choice model may avoid spending much time and money by gathering limited data on regular patronage patterns. In addition to this practical implication, the conclusions suggest that regular shopping destinations are chosen in accordance with the same behavioral motives as ancillary destinations are.

scholarly journals ESTIMATING THE DEMAND FOR BROADBAND SERVICES IN BRAZIL: ANALYSIS BY A DISCRETE-CHOICE MODEL

2019 ◽  
Vol 23 (2) ◽  
Author(s):  
Tainá Leandro ◽  
Victor Gomes
Keyword(s):  
Market Share ◽  
Discrete Choice ◽  
Price Elasticity ◽  
Choice Model ◽  
Discrete Choice Model ◽  
Discrete Choice Models ◽  
Elastic Demand ◽  
Broadband Services ◽  
Demand Price Elasticity ◽  
Positive Effect

ABSTRACT Discrete-choice models were used to estimate the demand for broadband services in Brazil. Results indicate an elastic demand for fixed broadband. The demand price elasticity is greater for municipalities that rely on more than one economic group offering broadband: the monopoly will set the price for the broadband services to minimize competition between plans in the same category. Therefore, consumer’s capability to react to higher prices is reduced. In addition, the possibility of purchasing broadband services in tripleplay bundles has a positive effect on market share in those municipalities with two or more groups providing broadband services.

Sensitivity of Vehicle Market Share Predictions to Discrete Choice Model Specification

2014 ◽  
Vol 136 (12) ◽  
Author(s):  
C. Grace Haaf ◽  
Jeremy J. Michalek ◽  
W. Ross Morrow ◽  
Yimin Liu
Keyword(s):  
Market Share ◽  
Discrete Choice ◽  
Consumer Choice ◽  
Predictive Accuracy ◽  
Choice Model ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Model Fit ◽  
Model Specification ◽  
Vehicle Market

When design decisions are informed by consumer choice models, uncertainty in choice model predictions creates uncertainty for the designer. We investigate the variation and accuracy of market share predictions by characterizing fit and forecast accuracy of discrete choice models for the US light duty new vehicle market. Specifically, we estimate multinomial logit models for 9000 utility functions representative of a large literature in vehicle choice modeling using sales data for years 2004–2006. Each model predicts shares for the 2007 and 2010 markets, and we compare several quantitative measures of model fit and predictive accuracy. We find that (1) our accuracy measures are concordant: model specifications that perform well on one measure tend to also perform well on other measures for both fit and prediction. (2) Even the best discrete choice models exhibit substantial prediction error, stemming largely from limited model fit due to unobserved attributes. A naïve “static” model, assuming share for each vehicle design in the forecast year = share in the last available year, outperforms all 9000 attribute-based models when predicting the full market one year forward, but attribute-based models can predict better for four year forward forecasts or new vehicle designs. (3) Share predictions are sensitive to the presence of utility covariates but less sensitive to covariate form (e.g., miles per gallons versus gallons per mile), and nested and mixed logit specifications do not produce significantly more accurate forecasts. This suggests ambiguity in identifying a unique model form best for design. Furthermore, the models with best predictions do not necessarily have expected coefficient signs, and biased coefficients could misguide design efforts even when overall prediction accuracy for existing markets is maximized.

Theoretical, methodological and practical issues of choice models

2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta
Keyword(s):  
Discrete Choice ◽  
Model Building ◽  
Estimation Method ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Decision Makers ◽  
Model Formulation ◽  
Random Utility Maximization ◽  
The Relationship ◽  
Method Model

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

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