Please use this identifier to cite or link to this item:
Cetverikov, Denis
Wilhelm, Daniel
Kim, Dongwoo
Year of Publication: 
Series/Report no.: 
cemmap working paper No. CWP49/19
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 random- ness 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

Files in This Item:
562.53 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.