Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189733 
Year of Publication: 
2018
Series/Report no.: 
cemmap working paper No. CWP26/18
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This paper explores the effects of simulated moments on the performance of inference methods based on moment inequalities. Commonly used confidence sets for parameters are level sets of criterion functions whose boundary points may depend on sample moments in an irregular manner. Due to this feature, simulation errors can affect the performance of inference in non-standard ways. In particular, a (first-order) bias due to the simulation errors may remain in the estimated boundary of the confidence set. We demonstrate, through Monte Carlo experiments, that simulation errors can significantly reduce the coverage probabilities of confidence sets in small samples. The size distortion is particularly severe when the number of inequality restrictions is large. These results highlight the danger of ignoring the sampling variations due to the simulation errors in moment inequality models. Similar issues arise when using predicted variables in moment inequalities models. We propose a method for properly correcting for these variations based on regularizing the intersection of moments in parameter space, and we show that our proposed method performs well theoretically and in practice.
Subjects: 
Simulated moments
Moment inequalities
Smoothable convex functions
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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