  An important challenge in statistical analysis lies in controlling the
estimation bias when handling the ever-increasing data size and model
complexity of modern data settings. In this paper, we propose a reliable
estimation and inference approach for parametric models based on the Just
Identified iNdirect Inference estimator (JINI). The key advantage of our
approach is that it allows to construct a consistent estimator in a simple
manner, while providing strong bias correction guarantees that lead to accurate
inference. Our approach is particularly useful for complex parametric models,
as it allows to bypass the analytical and computational difficulties (e.g., due
to intractable estimating equation) typically encountered in standard
procedures. The properties of JINI (including consistency, asymptotic
normality, and its bias correction property) are also studied when the
parameter dimension is allowed to diverge, which provide the theoretical
foundation to explain the advantageous performance of JINI in increasing
dimensional covariates settings. Our simulations and an alcohol consumption
data analysis highlight the practical usefulness and excellent performance of
JINI when data present features (e.g., misclassification, rounding) as well as
in robust estimation.
