Inference Based On Conditional Moment Inequalities

In this paper, the authors propose an instrumental variable approach to constructing Confidence Sets (CS's) for the true parameter in models defined by conditional moment inequalities/equalities. They show that by properly choosing instrument functions, one can transform conditional moment inequalities/equalities into unconditional ones without losing identification power. Based on the unconditional moment inequalities/equalities, they construct CS's by inverting Cram?r-von Mises-type or Kolmogorov-Smirnov-type tests. Critical values are obtained using Generalized Moment Selection (GMS) procedures.

Provided by: Yale University Topic: Big Data Date Added: Jun 2010 Format: PDF

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