This study assessed the impact of alternative parameterizations for the estimation of variance components on practical predictions of breeding values with MACE. Interbull MACE Holstein evaluations for somatic cell count (April 2009) and protein yield (August 2007) were considered. The MACE model was expressed in terms of a random regression model, which facilitates exploitation of principal component and factor analytic approaches. Both methods allow a reduction of the number of parameters to be estimated and benefit from the more parsimonious variance structure. Genetic parameters from different approaches were very similar, when the optimal fit was used. Over-fitting did not affect the estimates, but increased estimation time, whereas fitting too few parameters affected bull rankings in different countries.