Handling Endogenous Regressors by Joint Estimation Using Copulas

By Sungho Park, Sachin Gupta. Marketing science (Providence, R.I.), 2012

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Endogeneity in Stochastic Frontier Models (2016, methodology): “An example is Park and Gupta (2012). They assume that the marginal distribution of is normal, and they use nonparametric density estimation to”
How Well Does Consumer-Based Brand Equity Align with Sales-Based Brand Equity and Marketing-Mix Response? (2017, methodology): “We also control for potential endogeneity resulting from other unobserved shocks using Gaussian copulas that directly model the joint distribution of the potentially endogenous regressors and the error term through control function terms (Park and Gupta 2012).”
Can doing good lead to doing poorly? Firm value implications of CSR in the face of CSI (2017, methodology): “They generate consistent parameter estimates even when the assumed normal distribution of the error term is not present (Park and Gupta 2012).”
Marketing Analytics for Data-Rich Environments (2016, positive): “Researchers have developed several instrument-free methods to help in situations in which no valid instruments can be found (Ebbes et al. 2005; Park and Gupta 2012).”
The influence of a digital strategy on the digitalization of new ventures: The mediating effect of digital capabilities and a digital culture (2021, methodology): “As we were unable to identify a potential bias with this method, we applied the Gaussian copula approach (Park & Gupta, 2012).”