Economists’ anxiety about official U.S. inflation data is growing. One major issue: They don’t have the numbers they need to ...

Next, we will briefly cover early methods for handling missing data, such as complete case analysis and single imputation techniques (mean, hot deck, etc.), and why in practice they can produce ...

On the other hand, hard data isn’t necessarily as hard as you think. This isn’t a criticism of the statistical agencies, which are doing the best they can and should be given more resources.

Missing data imputation is a critical process in data analysis, enabling researchers to infer plausible values for absent observations. Over recent decades, a variety of methods have emerged ...

Jerome P. Reiter, Trivellore E. Raghunathan, The Multiple Adaptations of Multiple Imputation, Journal of the American Statistical Association, Vol. 102, No. 480 (Dec., 2007), ... This article ...

In this white paper, Bloomberg researchers show the applicability of deep latent variable models (DLVMs) in ESG datasets, outperforming classical imputation models as well as classical predictive ...

Abstract. Multiple imputation (MI) can be used to address missing data at Level 2 in multilevel research. In this article, we compare joint modeling (JM) and the fully conditional specification (FCS) ...

Dividends. After the tax is taken out, companies can pay some of what’s left to shareholders as a dividend, one for each share. Last September Telstra paid shareholders a dividend of 15.5 cents ...