Abstract
This chapter provides a nontechnical summary of how to deal with endogeneity in regression models for marketing research applications. When researchers want to make causal inference of a marketing variable (e.g., price) on an outcome variable (e.g., sales), using observational data and a regression approach, they need the marketing variable to be exogenous. If the marketing variable is driven by factors unobserved by the researcher, such as the weather or other factors, then the assumption that the marketing variable is exogenous is not tenable, and the estimated effect of the marketing variable on the outcome variable may be biased. This is the essence of the endogeneity problem in regression models. The classical approach to address endogeneity is based on instrumental variables (IVs). IVs are variables that isolate the exogenous variation in the marketing variable. However, finding IVs of good quality is challenging. We discuss good practice in finding IVs, and we examine common IV estimation approaches, such as the two-stage least squares approach and the control function approach. Furthermore, we consider other implementation challenges, such as dealing with endogeneity when there is an interaction term in the regression model. Importantly, we also discuss when endogeneity matters and when it does not matter, as the “cure” to the problem can be worse than the “disease.”
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Notes
- 1.
Many demand models in marketing are nonlinear. At the end of this chapter, we briefly discuss nonlinear models. A popular nonlinear demand model to estimate price elasticities is the log-log demand model, where both the dependent and independent variables are the natural logs of the original variables, which can be estimated using standard approaches for linear regression models. Log-log models are also prone to an endogeneity problem.
- 2.
We use the cross-sectional setup in Eq. 1 as the leading example in this chapter. A similar logic applies to a time series setup (e.g., when we would view Eq. 1 as a time series model). However, this would require an additional discussion of dealing with potential autocorrelation in the model error terms, which is beyond the scope of this chapter. Therefore, we assume that the error terms εi are independent and identically distributed in this chapter.
- 3.
For the sake of simplicity in this example, we assume that the researcher estimates a constant elasticity model (e.g., using a log-log regression). The optimal price can then be computed as p∗ = c(β/(β + 1)), where c is marginal cost and β is the estimated price elasticity (Amoroso-Robinson theorem, e.g., Homburg et al. 2009, p. 181).
- 4.
We simplified their model here for sake of exposition. The full model of Dinner et al. (2014) splits online advertising into search advertising and banner ads, allows for advertising carryover effects, and for the effects of other covariates.
- 5.
This discussion is similar in spirit to the rationale behind mediation analysis. We refer the reader to chapter “Mediation Analysis in Experimental Research” in this handbook for more details.
- 6.
The option sigmamore specifies that the covariance matrices are based on the estimated error variance from the efficient OLS estimator. Stata’s online help provides more information (“help Hausman”).
- 7.
In case the variables are mean centered before they enter the product, i.e., \( \left({P}_i-\overline{P}\right)\left({X}_i-\overline{X}\right) \), we need to use the mean-centered interaction term on the left-hand side of (16) and on the right-hand side of (14), instead of PiXi. The IVs do not require mean centering as the first-stage predictions will not be affected by mean centering.
- 8.
Ascarza et al. (2017) leverage a field experiment to address endogeneity concerns in the context of a customer relationship management (CRM) campaign, customer targeting, and social influence.
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Ebbes, P., Papies, D., van Heerde, H.J. (2022). Dealing with Endogeneity: A Nontechnical Guide for Marketing Researchers. In: Homburg, C., Klarmann, M., Vomberg, A. (eds) Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-57413-4_8
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