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Addressing endogeneity in aggregate logit models with time-varying parameters for optimal retail-pricing

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  • Guhl, Daniel

Abstract

It is well known that price endogeneity is a severe problem in demand models for market-level data (e.g., aggregate logit models) because it leads to biased estimates and therefore incorrect managerial implications. If the price parameter varies over time, as is usually the case, the relevance of the issue increases because standard methods to correct endogeneity biases (e.g., generalized method of moments) fail. This paper presents a control function approach as a remedy. A comprehensive simulation study demonstrates this method’s suitability, such that addressing endogeneity with the control function approach is the best choice. Moreover, addressing the endogeneity problem incorrectly may be even more harmful than simply ignoring it. To further illustrate the control function approach, we analyze the demand for canned tuna using aggregate retailer-level data. Here, all utility parameters vary over time and price endogeneity is indeed an issue. Effectively addressing price endogeneity correct has positive economic consequences: a normative model analysis reveals that implementing the control function approach yields a 3 % increase in retailer profits.

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  • Guhl, Daniel, 2019. "Addressing endogeneity in aggregate logit models with time-varying parameters for optimal retail-pricing," European Journal of Operational Research, Elsevier, vol. 277(2), pages 684-698.
    • Handle: RePEc:eee:ejores:v:277:y:2019:i:2:p:684-698
    DOI: 10.1016/j.ejor.2019.02.058
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