AI

Abstract

Many empirical micro-economics studies rely on consumer panels. For example, TV and web metering panels track TV and online usage of individuals. Sometimes more than one panel are available although these panels use different metering technologies and are subject to varying degrees of missingness. The problem we consider here is how to combine imputation based on two panels which have similar but not identical statistical characteristics. In the US, we have two two-screen panels, panel A (TV + desktop) and panel B(desktop + mobile) which are both calibrated to the US internet population. We want to estimate a count of ad impressions across all three-screens. As desktop impressions are metered in both panels, we fit a joint imputation model by pooling observed desktop impression counts across panels. After imputation on panel B, we fit a truncated negative binomial hurdle regression of mobile impression count over desktop impression count, demographic information, etc. And then, for each panelist in the panel A, we predict his/her mobile impression counts. In this way, we 'impute' mobile impressions in the panel A to facilitate three-screens measurements.