Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241892 
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP17/20
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive. In particular, we show that the test asymptotically controls size at a polynomial rate, is non-conservative, and detects certain smooth local alternatives that converge to the null with the fastest possible rate. Our test is based on a data-driven bandwidth value and the critical value for the test takes this randomness into account. Monte Carlo simulations indicate that the test performs well in finite samples. In particular, the simulations show that the test controls size and, under some alternatives, is significantly more powerful than existing procedures.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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