We use a dynamic panel data model to generate density forecasts for daily active Covid-19
infections for a panel of countries/regions. Our specification that assumes the growth rate of …

There is an innate human tendency, one might call it the “league table mentality,” to
construct rankings. Schools, hospitals, sports teams, movies, and myriad other objects are …

Abstract Models of unobserved heterogeneity, or frailty as it is commonly known in survival
analysis, can often be formulated as semiparametric mixture models and estimated by …

This paper considers the problem of forecasting a collection of short time series using cross‐
sectional information in panel data. We construct point predictors using Tweedie's formula …

Empirical Bayes methods for Gaussian compound decision problems involving longitudinal
data are considered. The new convex optimization formulation of the nonparametric (Kiefer …

This paper proposes a new methodology for estimating teacher value-added. Rather than
imposing a normality assumption on unobserved teacher quality (as in the standard …

We study multiple testing in the normal means problem with estimated variances that are
shrunk through empirical Bayes methods. The situation is asymmetric in that a prior is …

This article constructs individual-specific density forecasts for a panel of firms or households
using a dynamic linear model with common and heterogeneous coefficients as well as cross …

Nonparametric maximum likelihood (NPML) for mixture models is a technique for estimating
mixing distributions that has a long and rich history in statistics going back to the 1950s, and …

We use a dynamic panel Tobit model with heteroskedasticity to generate forecasts for a
large cross‐section of short time series of censored observations. Our fully Bayesian …