Bayesian methods are developed for the seemingly unrelated regressions (SUR) model where the model order or structure is presumed random. In particular we consider the class of models that are linear in some basis space. This class includes standard linear regression as a special case, as well as those models that involve non-linear transformations of the explanatory variables through a set of ...

This study examines the relationship between working capital efficiency, aggressiveness of funding, and inventory efficiency with operating performance companies in Indonesia. uses 132 annual financial report data from 2004 to 2016. The research model was tested Seemingly Unrelated Regression (SUR) estimated using Feasible Generalized Least Squares (FGLS). Based on test results, there is a corr...

The aim of this study was to develop and evaluate models for predicting the carcass composition of lambs. Forty male lambs were slaughtered and their carcasses were cooled for 24 hours. The subcutaneous fat thickness was measured between the 12th and 13th rib and breast bone tissue thickness was taken in the middle of the second sternebrae. Left side of carcasses was dissected and the proportio...

In this paper, we investigate seemingly unrelated regression (SUR) models that allow the number of equations (N) to be large, and comparable observations in each equation (T). It is well known literature conventional SUR estimator, for example, generalized least squares (GLS) estimator Zellner (1962) does not perform well. As main contribution propose a new feasible GLS called graphical lasso (...

This paper develops models of the demand for and supply of elective (non-emergency) surgery using a panel of quarterly data for 200 English hospitals over the period 1995-2002. Unusually, distinct measures of supply (outpatients seen and inpatient admissions) and demand (outpatient referrals and decisions to admit) are available for each observation. These offer the opportunity to estimate sepa...

Spatial seemingly unrelated regression (spatial SUR) models are a useful multiequational econometric specification to simultaneously incorporate spatial effects and correlated error terms across equations. The purpose of the spsur R package is supply complete set functions test for structures in residual SUR model; estimate most popular specifications by applying different methods linear restri...