Hedonic analysis of Wisconsin Beef Improvement Association Bull Sale and Development Program data revealed buyer preferences for calving ease, growth, production weight, and carcass merit traits. Attributes like calving ease direct Expected Progeny Differences (EPD), average daily gain, birth to yearling gain EPD, and rib-eye area consistently ranked higher and significantly influenced the bull’s sale price. Further analysis using a 2-Class Finite Mixture Model indicated distinct groups of buyers in the region who prioritized measured bull attributes and others who did not, confirming heterogeneity in buyer preferences.

We study the effects of taxation on the growth rate of the real per capita GDP in a sample of 21 OECD countries, over the period 1965–2010. To do this, we estimate a version of the model proposed by Mankiw, Romer and Weil [(1992) Quarterly Journal of Economics 107, 407–437.] augmented to consider both direct and indirect effects of taxation on investment share parameters. We employ a semi-parametric technique—namely, a Finite Mixture Model—which combines features from mixed effect models for panel data and cluster analysis methods to account for country-specific unobserved heterogeneity. Our results suggest that taxes have a negative impact on growth: in the baseline model, the coefficient estimates indicate that a 10% cut in personal income tax rate (respectively corporate income tax rate) may raise the GDP growth rate by 0.6% (respectively 0.3%).

Consumers prefer bright, cherry-red retail beef. Retailers often mark down the price of discolored beef for quick sale. However, following this practice could result in a net loss of revenue if consumer willingness to pay (WTP) for nondiscolored beef is negatively affected by the presence of discolored beef in the consumer choice set. Through a hypothetical online survey and a controlled in-person experiment, we determine that marketing discolored beef together with nondiscolored beef increases most consumers’ evaluation of, but not their WTP for, nondiscolored beef.

The latent position cluster model is a popular model for the statistical analysis of network data. This model assumes that there is an underlying latent space in which the actors follow a finite mixture distribution. Moreover, actors which are close in this latent space are more likely to be tied by an edge. This is an appealing approach since it allows the model to cluster actors which consequently provides the practitioner with useful qualitative information. However, exploring the uncertainty in the number of underlying latent components in the mixture distribution is a complex task. The current state-of-the-art is to use an approximate form of BIC for this purpose, where an approximation of the log-likelihood is used instead of the true log-likelihood which is unavailable. The main contribution of this paper is to show that through the use of conjugate prior distributions, it is possible to analytically integrate out almost all of the model parameters, leaving a posterior distribution which depends on the allocation vector of the mixture model. This enables posterior inference over the number of components in the latent mixture distribution without using trans-dimensional MCMC algorithms such as reversible jump MCMC. Our approach is compared with the state-of-the-art latentnet (Krivitsky & Handcock, 2015) and VBLPCM (Salter-Townshend & Murphy, 2013) packages.