  We investigate predictive density estimation under the $L^2$ Wasserstein loss
for location families and location-scale families. We show that plug-in
densities form a complete class and that the Bayesian predictive density is
given by the plug-in density with the posterior mean of the location and scale
parameters. We provide Bayesian predictive densities that dominate the best
equivariant one in normal models.
