Estimation and bootstrap for stochastically monotone Markov processes - Michael H. Neumann (Metrika, 2023)
The Markov property is shared by several popular models for time series such as autoregressive or integer-valued autoregressive processes as well as integer-valued ARCH processes. A natural assumption which is fulfilled by corresponding parametric versions of these models is that the random variable at time~ t gets stochastically greater conditioned on the past, as the value of the random variable at time~ t-1increases. Then the associated family of conditional distribution functions has a certain monotonicity property which allows us to employ a nonparametric antitonic estimator. This estimator does not involve any tuning parameter which controls the degree of smoothing and is therefore easy to apply. Nevertheless, it is shown that it attains a rate of convergence which is known to be optimal in similar cases. This estimator forms the basis for a new method of bootstrapping Markov chains which inherits the properties of simplicity and consistency from the underlying estimator of the conditional distribution function.
Keywords: 510; Autoregressive process; Bootstrap; INAR; Integer-valued ARCH; Markov chain; Stochastic order