Omitted budget constraint bias in discrete-choice demand models

Omitted Product Attributes in Discrete Choice Models

We describe two methods for correcting an omitted variables problem in discrete choice models: a fixed effects approach and a control function approach. The control function approach is easier to implement and applicable in situations for which the fixed effects approach is not. We apply both methods to a cross-section of disaggregate data on customer’s choice among television options including...

Panel Data Discrete Choice Models of Consumer Demand

1. Introduction This chapter deals with the vast literature on panel data discrete choice models of consumer demand. The reason this area is so active is that very high quality data is available. Firms like Nielsen and IRI have, for over 30 years, been collecting panel data on households' purchases of consumer goods. This is known as ―scanner data,‖ because it is collected by check-out machine ...

Random Utility-Based Discrete Choice Models for Travel Demand Analysis

Estimating Discrete Choice Models of Demand with Market Level Variation

In this paper we develop and apply a new empirical approach to estimating a widely used class of models of demand for differentiated products. Our approach is applicable to the often encountered situation where the researcher has data on a sample of markets (i.e., the “many markets” setting). Our focus on the “many markets” setting stands in contrast to the “many products” setting that was famo...

Assessing Omitted Confounder Bias in Multilevel Mediation Models.

To draw valid inference about an indirect effect in a mediation model, there must be no omitted confounders. No omitted confounders means that there are no common causes of hypothesized causal relationships. When the no-omitted-confounder assumption is violated, inference about indirect effects can be severely biased and the results potentially misleading. Despite the increasing attention to ad...