  Small area estimation has received enormous attention in recent years due to
its wide range of application, particularly in policy making decisions. The
variance based on direct sample size of small area estimator is unduly large
and there is a need of constructing model based estimator with low mean squared
prediction error (MSPE). Estimation of MSPE and in particular the bias
correction of MSPE plays the central piece of small area estimation research.
In this article, a new technique of bias correction for the estimated MSPE is
proposed. It is shown that that the new MSPE estimator attains the same level
of bias correction as the existing estimators based on straight Taylor
expansion and jackknife methods. However, unlike the existing methods, the
proposed estimate of MSPE is always nonnegative. Furthermore, the proposed
method can be used for general two-level small area models where the variables
at each level can be discrete or continuous and, in particular, be nonnormal.
