scholarly journals Identification and Estimation in Discrete Choice Demand Models when Endogenous Variables Interact with the Error

2011 ◽  
Author(s):  
Amit Gandhi ◽  
Kyoo il Kim ◽  
Amil Petrin
Keyword(s):  
Discrete Choice ◽  
Demand Models ◽  
Endogenous Variables ◽  
Discrete Choice Demand

Enhancing Discrete Choice Demand Modeling for Decision-Based Design

2003 ◽  
Author(s):  
Henk Jan Wassenaar ◽  
Wei Chen ◽  
Jie Cheng ◽  
Agus Sudjianto
Keyword(s):  
Discrete Choice ◽  
Market Research ◽  
Ford Motor Company ◽  
Demand Model ◽  
Demand Modeling ◽  
Product Demand ◽  
Demand Models ◽  
Discrete Choice Demand ◽  
Decision Based Design ◽  
Key Customer

Our research is motivated by the need for developing a rigorous Decision-Based Design framework and the need for developing an approach to demand modeling that is critical for assessing the profit a product can bring. Even though demand modeling techniques exist in market research, little work exists on product demand modeling that addresses the specific needs of engineering design in particular that facilitates engineering decision-making. Building upon our earlier work on using the discrete choice analysis approach to demand modeling, in this work, we provide detailed guidelines for implementing the discrete choice demand modeling approach in product design. The modeling of a hierarchy of product attributes is introduced to cascade customer desires to specific key customer attributes that can be represented using engineering language. To improve the predictive capability of demand models, we propose to use the Kano method for providing the econometric justification when selecting the shape of the customer utility function. A real (passenger) vehicle engine case study, developed in collaboration with the market research firm J.D. Power & Associates and Ford Motor Company, demonstrates the proposed approaches. The example focuses on demand analysis and does not reach beyond the key customer attribute level. The obtained demand model is shown to be satisfactory through cross validation.

Understanding Grain Movements for Demand Estimation

2005 ◽  
Vol 1909 (1) ◽  
pp. 17-21 ◽  
Author(s):  
Eric Jessup ◽  
Ken Casavant
Keyword(s):  
Pacific Northwest ◽  
Stated Preference ◽  
Transportation Network ◽  
Snake River ◽  
Revealed Preferences ◽  
Empirical Estimation ◽  
Demand Models ◽  
Volume Response ◽  
Grain Marketing ◽  
Discrete Choice Demand

Grain producers and handlers in Washington State have benefited from a multimodal transportation network of roads, railroads, and the Columbia–Snake River barge system to move large amounts of grain effectively in a timely and economic manner. The competitive environment of the grain industry brings many changes, including the number of firms and houses, mergers, and modal competitiveness. Additionally, marketing strategies are affected because choices of available transportation modes reflect the decision processes of warehouses or firm managers. This aggregate study of grain marketing and transportation in the Pacific Northwest helps lay the groundwork for subsequent estimates of empirical demand. Such subsequent modeling attempts may include revealed and stated preference analysis in discrete choice demand models. A thorough understanding of the industry and market characteristics should improve empirical estimation efforts and produce more defensible policy analysis. Based on a 90% shipment volume response rate, results show that in the Columbia–Snake River grain situation, one destination absorbs more than 90% of shipments. Modal competition is active; barge has a market share of more than 50%, down 12–16% from 10 years ago. Multiple-car shipments have increased, but not drastically. Rates are consistently competitive over the period. Finally, grain demand is seasonal but generally has been stable over time. The revealed preferences from this aggregate analysis suggest that price elasticity may vary across shippers, times of movement, and modal availability.

scholarly journals Heating Costs and Household Wood Stove Acquisition: A Discrete Choice Demand Model

1985 ◽  
Vol 14 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Paul Francis Scodari ◽  
Ian W. Hardie
Keyword(s):  
New Hampshire ◽  
Discrete Choice ◽  
Choice Model ◽  
Operating Costs ◽  
Demand Model ◽  
Wood Stove ◽  
Home Heating ◽  
Fuel Burning ◽  
Discrete Choice Demand ◽  
Wood Stoves

This paper examines the acquisition of wood stoves by New Hampshire households through use of a utility-maximizing discrete choice model. The analysis is based on the hypothesis that wood stoves are acquired to decrease the monetary costs of home-heating. Operating costs associated with heating with conventional fuel burning capital and with a combination of conventional and wood stove heating capital are estimated. These operating costs are used to estimate probabilities of 1979 wood stove acquisition for particular types of New Hampshire households.

A Simulation-Based Heuristic to Find Approximate Equilibria with Disaggregate Demand Models

2021 ◽  
Vol 55 (5) ◽  
pp. 1025-1045
Author(s):  
Stefano Bortolomiol ◽  
Virginie Lurkin ◽  
Michel Bierlaire
Keyword(s):  
Discrete Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Added Value ◽  
Mixed Integer ◽  
Iteration Algorithm ◽  
Oligopolistic Competition ◽  
Integer Optimization ◽  
Equilibrium Solutions ◽  
Demand Models

Oligopolistic competition occurs in various transportation markets. In this paper, we introduce a framework to find approximate equilibrium solutions of oligopolistic markets in which demand is modeled at the disaggregate level using discrete choice models, according to random utility theory. Compared with aggregate demand models, the added value of discrete choice models is the possibility to account for more complex and precise representations of individual behaviors. Because of the form of the resulting demand functions, there is no guarantee that an equilibrium solution for the given market exists, nor is it possible to rely on derivative-based methods to find one. Therefore, we propose a model-based algorithmic approach to find approximate equilibria, which is structured as follows. A heuristic reduction of the search space is initially performed. Then, a subgame equilibrium problem is solved using a mixed integer optimization model inspired by the fixed-point iteration algorithm. The optimal solution of the subgame is compared against the best responses of all suppliers over the strategy sets of the original game. Best response strategies are added to the restricted problem until all ε-equilibrium conditions are satisfied simultaneously. Numerical experiments show that our methodology can approximate the results of an exact method that finds a pure equilibrium in the case of a multinomial logit model of demand with a single-product offer and homogeneous demand. Furthermore, it succeeds at finding approximate equilibria for two transportation case studies featuring more complex discrete choice models, heterogeneous demand, a multiproduct offer by suppliers, and price differentiation for which no analytical approach exists.

Enhancing Discrete Choice Demand Modeling for Decision-Based Design

2005 ◽  
Vol 127 (4) ◽  
pp. 514 ◽  
Author(s):  
Henk Jan Wassenaar ◽  
Wei Chen ◽  
Jie Cheng ◽  
Agus Sudjianto
Keyword(s):  
Discrete Choice ◽  
Demand Modeling ◽  
Discrete Choice Demand ◽  
Decision Based Design

A Comparative Empirical Study of Discrete Choice Models in Retail Operations

2021 ◽  
Author(s):  
Gerardo Berbeglia ◽  
Agustín Garassino ◽  
Gustavo Vulcano
Keyword(s):  
Empirical Study ◽  
Discrete Choice ◽  
Operations Management ◽  
Latent Class ◽  
Markov Chain Model ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Chain Model ◽  
Retail Operations ◽  
Demand Models

Choice-based demand estimation is a fundamental task in retail operations and revenue management, providing necessary input data for inventory control, assortment, and price-optimization models. The task is particularly difficult in operational contexts where product availability varies over time and customers may substitute into the available options. In addition to the classical multinomial logit (MNL) model and extensions (e.g., nested logit, mixed logit, and latent-class MNL), new demand models have been proposed (e.g., the Markov chain model), and others have been recently revisited (e.g., the rank list-based and exponomial models). At the same time, new computational approaches were developed to ease the estimation function (e.g., column-generation and expectation-maximization (EM) algorithms). In this paper, we conduct a systematic, empirical study of different choice-based demand models and estimation algorithms, including both maximum-likelihood and least-squares criteria. Through an exhaustive set of numerical experiments on synthetic, semisynthetic, and real data, we provide comparative statistics of the predictive power and derived revenue performance of an ample collection of choice models and characterize operational environments suitable for different model/estimation implementations. We also provide a survey of all the discrete choice models evaluated and share all our estimation codes and data sets as part of the online appendix. This paper was accepted by Vishal Gaur, operations management.

Sign in / Sign up

Export Citation Format

Share Document