Abstract:
In this article, we consider the multiple regression model in the presence
of multicollinearity and study the performance of the preliminary
test estimator (PTE) both analytically and computationally, when it is a
priori suspected that some constraints may hold on the vector parameter
space. The performance of the PTE is further analyzed by comparing
the risk of some well-known estimators of the ridge parameter through
an extensive Monte Carlo simulation study under some bounded and or
asymmetric loss functions. An application of the Cobb–Douglas production
function is included and from these results as well as the simulation
studies, it is clear that the bounded linear exponential loss function outperforms
the other loss functions across all the proposed ridge parameters
by comparing the risk values.
