  In this paper we propose a flexible nested error regression small area model
with high dimensional parameter that incorporates heterogeneity in regression
coefficients and variance components. We develop a new robust small area
specific estimating equations method that allows appropriate pooling of a large
number of areas in estimating small area specific model parameters. We propose
a parametric bootstrap and jackknife method to estimate not only the mean
squared errors but also other commonly used uncertainty measures such as
standard errors and coefficients of variation. We conduct both modelbased and
design-based simulation experiments and real-life data analysis to evaluate the
proposed methodology
