Absolute regularity and ergodicity of Poisson count processes
arXiv:1201.1071 · doi:10.3150/10-BEJ313
Abstract
We consider a class of observation-driven Poisson count processes where the current value of the accompanying intensity process depends on previous values of both processes. We show under a contractive condition that the bivariate process has a unique stationary distribution and that a stationary version of the count process is absolutely regular. Moreover, since the intensities can be written as measurable functionals of the count variables, we conclude that the bivariate process is ergodic. As an important application of these results, we show how a test method previously used in the case of independent Poisson data can be used in the case of Poisson count processes.
Published in at http://dx.doi.org/10.3150/10-BEJ313 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
Cited by in corpus (14)
- Theory and Inference for a Class of Observation-driven Models with Application to Time Series of Counts
- Tests for Time Series of Counts Based on the Probability Generating Function
- Absolute regularity of semi-contractive GARCH-type processes
- Inference and testing for structural change in time series of counts model
- New goodness-of-fit diagnostics for conditional discrete response models
- Multivariate Count Autoregression
- The maximizing set of the asymptotic normalized log-likelihood for partially observed Markov chains
- Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator
- Self-excited Threshold Poisson Autoregression
- A robust approach for testing parameter change in Poisson autoregressive models
- A test for counting sequences of integer-valued autoregressive models
- Two step estimations via the Dantzig selector for models of stochastic processes with high-dimensional parameters
- General-order observation-driven models: ergodicity and consistency of the maximum likelihood estimator
- Handy sufficient conditions for the convergence of the maximum likelihood estimator in observation-driven models