Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/307485 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Business Economics [ISSN:] 1861-8928 [Volume:] 94 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 639-657
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
A regressor is endogenous if it is correlated with the unobserved residual of a model. Ignoring endogeneity may lead to biased coefficients. We deal with the omitted variable bias that arises if firms set marketing variables considering factors (demand shocks) that researchers do not observe. Whereas publications on sales response or brand choice models frequently take the potential endogeneity of marketing variables into account, multicategory choice models provide a different picture. To consider endogeneity in multicategory choice models, we follow a two-step Gaussian copula approach. The first step corresponds to an individual-level random coefficient version of the multivariate logit model. We analyze yearly shopping data for one specific grocery store, referring to 29 product categories. If the assumption of a Gaussian correlation structure is met, the copula approach indicates the endogeneity of a category-specific marketing variable in about 31% of the categories. The majority of marketing variables rated as endogenous are positively correlated with the omitted variable, implying that ignoring endogeneity leads to an overestimation of the coefficients of the respective marketing variable. Finally, we investigate whether taking endogeneity into account by the copula approach leads to different managerial implications. In this regard, we demonstrate that for our data ignoring endogeneity often suggests a level of marketing activity that is too high.
Subjects: 
Marketing
Retailing
Multicategory choice
Market basket analysis
Endogeneity
JEL: 
M31
L81
D1
C35
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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