Empirical phi-divergence test statistics for testing simple null hypotheses based on exponentially tilted empirical likelihood estimators
Methodology
2015-10-29 v2
Abstract
In Econometrics, imposing restrictions without assuming underlying distributions to modelize complex realities is a valuable methodological tool. However, if a subset of restrictions were not correctly specified, the usual test-statistics for correctly specified models tend to reject erronously a simple null hypothesis. In this setting, we may say that the model suffers from misspecification. We study the behavior of empirical phi-divergence test-statistics, introduced in Balakrishnan et al. (2015), by using the exponential tilted empirical likelihood estimators of Schennach (2007), as a good compromise between efficiency of the significance level for small sample sizes and robustness under misspecification.
Cite
@article{arxiv.1503.00994,
title = {Empirical phi-divergence test statistics for testing simple null hypotheses based on exponentially tilted empirical likelihood estimators},
author = {Angel Felipe and Nirian Martín and Pedro Miranda and Leandro Pardo},
journal= {arXiv preprint arXiv:1503.00994},
year = {2015}
}