ADAPTIVE TESTS OF CONDITIONAL MOMENT INEQUALITIES
基于核估计构造了条件矩不等式模型参数假设的新检验，能自动适应矩函数的光滑性，具有一致正确的渐近尺寸，并对某些备择假设达到速率最优。
Many economic models yield conditional moment inequalities that can be used for inference on parameters of these models. In this paper, I construct new tests of parameter hypotheses in conditional moment inequality models based on studentized kernel estimates of moment functions. The tests automatically adapt to the unknown smoothness of the moment functions, have uniformly correct asymptotic size, and are rate-optimal against certain classes of alternatives. Some existing tests have nontrivial power against n −1/2 -local alternatives of a certain type whereas my methods only allow for nontrivial testing against ( n / log n ) −1/2 -local alternatives of this type. There exist, however, large classes of sequences of well-behaved alternatives against which the tests developed in this paper are consistent and those tests are not.