Consumer Choice Models and Estimation: A Review and Extension

2021 ◽  
Author(s):  
Qi Feng ◽  
J. George Shanthikumar ◽  
Mengying Xue
Keyword(s):  
Consumer Choice ◽  
Choice Models

Data pruning in consumer choice models

2006 ◽  
Vol 4 (3) ◽  
pp. 267-287 ◽  
Author(s):  
Elaine L. Zanutto ◽  
Eric T. Bradlow
Keyword(s):  
Consumer Choice ◽  
Choice Models ◽  
Data Pruning

Operations Management under Consumer Choice Models with Multiple Purchases

2020 ◽  
Author(s):  
Shujie Luan ◽  
Ruxian Wang ◽  
Xiaolin Xu ◽  
Weili Xue
Keyword(s):  
Operations Management ◽  
Consumer Choice ◽  
Choice Models

Consumer Choice Models with Endogenous Network Effects

2017 ◽  
Vol 63 (11) ◽  
pp. 3944-3960 ◽  
Author(s):  
Ruxian Wang ◽  
Zizhuo Wang
Keyword(s):  
Consumer Choice ◽  
Network Effects ◽  
Choice Models

INFLUENCE OF OMITTED VARIABLES IN CONSUMER CHOICE MODELS ON ENGINEERING DESIGN OPTIMIZATION SOLUTIONS

2021 ◽  
pp. 1-20
Author(s):  
Waleed Gowharji ◽  
Kate Whitefoot
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Consumer Choice ◽  
Optimization Problems ◽  
Revealed Preference ◽  
Choice Models ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
Engineering Design Optimization ◽  
The Impact

Abstract This paper examines the impact of Omitted Variable Bias (OVB) within consumer choice models on engineering design optimization solutions. Engineering products often have a multitude of attributes that influence consumers' purchasing decisions, many of which are difficult to include in revealed-preference models due to a lack of data. Correlations among these omitted variables and product attributes included in the model can bias demand parameter estimates. However, engineering design optimization studies typically do not account for this bias. We examine the influence consumer-choice OVB can have on design optimization results. We first mathematically derive how OVB propagates into optimal design solutions and characterize properties of optimization problems that affect the magnitude of the resulting error in solutions. We then demonstrate the impact of OVB on optimal designs using an engineering optimization case study of automotive powertrain design. In the demonstration, we estimate two sets of choice models: one using only “typically observed” vehicle attributes commonly found in the literature, and one with an additional set of “typically unobserved” attributes gathered from Edmunds.com. We find that the model with omitted variables leads to, in some scenarios, substantial bias in parameter estimates (5-143%), which propagates up to 21% error in the optimal engine size.

Influence of Omitted Variables in Consumer Choice Models on Engineering Design Optimization Solutions

2019 ◽  
Author(s):  
Waleed Gowharji ◽  
Kate S. Whitefoot
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Consumer Choice ◽  
Optimization Problems ◽  
Revealed Preference ◽  
Choice Models ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
Engineering Design Optimization ◽  
The Impact

Abstract This paper examines the impact of Omitted Variable Bias (OVB) within consumer choice models on engineering design optimization solutions. Engineering products often have a multitude of attributes that influence consumers’ purchasing decisions, many of which are difficult to include in revealed-preference models due to a lack of data. Correlations among these omitted variables and product attributes included in the model can bias demand parameter estimates. However, engineering design optimization studies typically do not account for this bias. We examine the influence consumer-choice OVB can have on design optimization results. We first mathematically derive how OVB propagates into biased optimal design solutions and characterize properties of optimization problems that affect the magnitude of this bias. We then demonstrate the impact of OVB on optimal designs using a more-realistic engineering optimization case study of automotive powertrain design. In the demonstration, we estimate two sets of choice models: one using only “typically observed” vehicle attributes commonly found in the literature, and one with an additional set of “typically unobserved” attributes gathered from Edmunds.com. We find that the model with omitted variables leads to, in some scenarios, substantial bias in parameter estimates (5–143%) which propagates up to 21% bias in the optimal engine size.

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