Specification Test based on Convolution-type Distribution Function Estimates for Non-linear Auto-regressive Processes
Statistics Theory
2016-03-03 v1 Statistics Theory
Abstract
The paper proposes a specification test based on two estimates of distribution function. One is the traditional kernel distribution function estimate and the other is a newly proposed convolution-type distribution function estimate. Asymptotic properties of the new estimate are studied when the innovation density is known and when it is unknown. The MISE-type statistic based on these estimates is suggested to test parametric specifications of the mean and volatility functions. The relating asymptotic results are obtained and the finite-sample properties are studied based on the bootstrap methodology. A simulation study shows that the proposed test competes favorably to benchmark tests in terms of the empirical level and power.
Cite
@article{arxiv.1603.00800,
title = {Specification Test based on Convolution-type Distribution Function Estimates for Non-linear Auto-regressive Processes},
author = {Kun Ho Kim and Jiwoong Kim},
journal= {arXiv preprint arXiv:1603.00800},
year = {2016}
}