Regression and Endogeneity Bias in Big Marketing Data

Endogeneity in Marketing Decision Models

T are many critical concerns (including the accounting for endogeneity) when one is properly estimating response functions. However, it is sometimes (certainly not always) better to leave some variables exogenous when building mathematical models intended to help decision makers. The exogenous variables allow the decision maker to better adapt the mathematical model to different situations and ...

Endogeneity in Logistic Regression Models

Menteshashvili O, et al. Pulmonary tuberculosis in prisons of the ex-USSR state Georgia: results of a nationwide prevalence survey among sentenced inmates. Antimicrobial prescribing patterns for respiratory diseases including tuberculosis in Russia: a possible role in drug resistance? Jpyridinium chloride is useful for isolation of Mycobacterium tuberculosis from sputa subjected to long-term st...

Editorial - Marketing Science and Big Data

Endogeneity in Nonparametric and Semiparametric Regression Models

This paper considers the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors. We list a number of different generalizations of the linear structural equation model, and discuss how three common estimation approaches for linear equations — the “instrumental variables,” “fitted value,” and “control function” approaches — may or may not b...

Investigating Endogeneity Bias in Conjoint Models

The use of adaptive designs in conjoint analysis has been shown to lead to an endogeneity bias in part-worth estimates using sampling experiments. In this paper, we re-examine the endogeneity issue in light of the likelihood principle. The likelihood principle asserts that all relevant information in the data about model parameters is contained in the likelihood function. We show that adhering ...