The concept of seemingly unrelated models is used for multivariate observations when the components of the multivariate dependent variable are governed by mutually different sets of explanatory variables and the only relation between the components is given by a fixed covariance within the observational units. A multivariate weighted least squares estimator is employed which takes the within un...

We present a sparse seemingly unrelated regression (SSUR) model to generate substantively relevant structures in the high-dimensional distributions of seemingly unrelated model (SUR) parameters. This SSUR framework includes prior specifications, posterior computations using Markov chain Monte Carlo methods, evaluations of model uncertainty, and model structure searches. Extensions of the SSUR m...

The linear and nonlinear seemingly unrelated regression problem with general error distribution is analyzed using recent likelihood theory that arguably provides the definitive distribution for assessing a scalar parameter; this involves implicit but well defined conditioning and marginalization for determining intrinsic measures of departure. Highly accurate p-values are obtained for the key d...

We investigate a Bayesian approach to modelling the statistical association between markers at multiple loci and multivariate quantitative traits. In particular, we describe the use of Bayesian Seemingly Unrelated Regressions (SUR) whereby genotypes at the different loci are allowed to have non-simultaneous effects on the phenotypes considered with residuals from each regression assumed correla...

A method was developed for estimating parameters in an individual tree basal area growth model using a system of equations based on dbh rank classes. The estimation method developed is a compromise between an individual tree and a stand level basal area growth model that accounts for the correlation between trees within a plot by using seemingly unrelated regression (SUR) to estimate the restri...

The negative CAPM alphas of high-beta and high-variance stocks are attributable to an unaccounted factor in the CAPM. We use eight seemingly unrelated anomalies construct a composite spirit optimal orthogonal portfolio (FOP). Accounting for FOP re-establishes positive relation between beta average returns time series regressions as well cross-sectional explains stocks. To analyze economic drive...