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Title: Testing parameters of models with conditional moment restrictions using empirical likelihood Authors:  Yves Berger - University of Southampton (United Kingdom) [presenting]
Abstract: An empirical likelihood test is proposed for models defined by conditional moment restrictions, such as models with non-linear endogenous covariates, with or without heteroscedastic errors or non-separable transformation models. The number of empirical likelihood constraints is given by the size of the parameter, unlike alternative approaches. We show that the empirical likelihood ratio test is asymptotically pivotal, without explicit studentisation. For moderate sample sizes, we show that this property may not hold with alternative empirical likelihood approaches. The proposed test clearly outperforms alternative empirical likelihood tests. It also offers a major advantages over two-stage least-squares, because the relationship between the endogenous and instrumental variables does not need to be known.