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An Adaptive Test of Stochastic Monotonicity

Chetverikov, D; Wilhelm, D; Kim, D; (2020) An Adaptive Test of Stochastic Monotonicity. Econometric Theory 10.1017/S0266466620000225. (In press). Green open access

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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 nonconservative, 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.

Type: Article
Title: An Adaptive Test of Stochastic Monotonicity
Open access status: An open access version is available from UCL Discovery
DOI: 10.1017/S0266466620000225
Publisher version: http://doi.org/10.1017/S0266466620000225
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10095663
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