AI

How Many Millennials Visit YouTube? Estimating Unobserved Events From Incomplete Panel Data Conditioned on Demographic Covariates

Abstract

Many socio-economic studies rely on panel data as they also provide detailed demographic information about consumers. For example, advertisers use TV and web metering panels to estimate ads effectiveness in selected target demographics. However, panels often record only a fraction of all events due to non-registered devices, technical problems, or work usage. Goerg et al. (2015) present a beta-binomial negative-binomial hurdle (BBNBH) model to impute missing events in count data with excess zeros.

In this work, we study empirical properties of the MLE for the BBNBH model, extend it to categorical covariates, introduce a penalized maximum likelihood estimator (MLE) to get accurate estimates by demographic group, and apply the methodology to a German media panel to learn about demographic patterns in the YouTube viewership.