Bounds for inference with nuisance parameters present only under the alternative

In hypothesis testing with nuisance parameters present only under the alternative two issues typically arise: (i) critical values are data dependent and so cannot be tabulated; (ii) we need to choose a functional over the nuisance parameter space. We address the first issue by providing easily computable bounds for the case of dependent and heterogeneous observations. We tackle the second issue by suggesting a weighted average statistic with weights given by the (quasi) likelihood over the nuisance parameter space. The small sample behavior of our procedure is analyzed via Monte Carlo simulations; we consider conditional moment tests and tests for nonlinearities in the SETAR model. An empirical illustration to the modeling of US male unemployment is provided. Copyright Royal Economic Society, 2002