TWO-STEP EMPIRICAL LIKELIHOOD ESTIMATION UNDER STRATIFIED SAMPLING WHEN AGGREGATE INFORMATION IS AVAILABLE

Empirical likelihood is appropriate to estimate moment condition models when a random sample from the target population is available. However, many economic surveys are subject to some form of stratification, in which case direct application of empirical likelihood will produce inconsistent estimators. In this paper we propose a two-step empirical likelihood estimator to deal with stratified samples in models defined by unconditional moment restrictions in the presence of some aggregate information such as the mean and the variance of the variable of interest. A Monte Carlo simulation study reveals promising results for many versions of the two-step empirical likelihood estimator. Copyright © 2006 The Authors; Journal compilation © 2006 Blackwell Publishing Ltd and The University of Manchester.