Data Enrichment and Cross Panel Imputation
Venue
Google, Inc. (2016), pp. 1-18 (to appear)
Publication Year
2016
Authors
Yunting Sun, Jim Koehler, Nicolas Remy, Wiesner Vos
BibTeX
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.
