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CWP1720-An-Adaptive-Test-of-Stochastic-Monotonicity.pdf
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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.
Authors
Research Associate LMU Munich
Daniel is a Research Associate of the IFS in Cemmap and Professor of Statistics and Econometrics at LMU Munich.
UCLA
Dongwoo Kim
Working Paper details
- DOI
- 10.1920/wp.cem.2020.1720
- Publisher
- The IFS
Suggested citation
D, Chetverikov and D, Kim and D, Wilhelm. (2020). An Adaptive Test of Stochastic Monotonicity. London: The IFS. Available at: https://ifs.org.uk/publications/adaptive-test-stochastic-monotonicity-1 (accessed: 25 April 2024).
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