Piecewise autoregression for general integer-valued time series
Abstract
This paper proposes a piecewise autoregression for general integer-valued time series. The conditional mean of the process depends on a parameter which is piecewise constant over time. We derive an inference procedure based on a penalized contrast that is constructed from the Poisson quasi-maximum likelihood of the model. The consistency of the proposed estimator is established. From practical applications, we derive a data-driven procedure based on the slope heuristic to calibrate the penalty term of the contrast; and the implementation is carried out through the dynamic programming algorithm, which leads to a procedure of time complexity. Some simulation results are provided, as well as the applications to the US recession data and the number of trades in the stock of Technofirst.
Cite
@article{arxiv.1911.00989,
title = {Piecewise autoregression for general integer-valued time series},
author = {Mamadou Lamine Diop and William Kengne},
journal= {arXiv preprint arXiv:1911.00989},
year = {2019}
}