Bias corrections for moment estimators in Poisson INAR(1) and INARCH(1) processes
DOI10.1016/j.spl.2016.01.018zbMath1419.62253OpenAlexW2253183391MaRDI QIDQ273793
Sebastian Schweer, Christian H. Weiß
Publication date: 22 April 2016
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2016.01.018
asymptotic propertiesbias correctionscount-data time seriesmoment estimatorsPoisson INAR(1) modelPoisson INARCH(1) model
Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05) Markov processes: estimation; hidden Markov models (62M05)
Related Items (8)
Uses Software
Cites Work
- Unnamed Item
- Modelling time series of counts with overdispersion
- Estimation and testing for a Poisson autoregressive model
- Absolute regularity and ergodicity of Poisson count processes
- On weak dependence conditions: the case of discrete valued processes
- Estimation in conditional first order autoregression with discrete support
- Discrete analogues of self-decomposability and stability
- Multivariate statistical modelling based on generalized linear models. With contributions by Wolfgang Hennevogl
- Compound Poisson INAR(1) processes: stochastic properties and testing for overdispersion
- Corrigendum to: ``On weak dependence conditions: the case of discrete valued processes
- Parameter estimation for binomial \(\mathrm{AR}(1)\) models with applications in finance and industry
- Asymptotic properties of CLS estimators in the Poisson AR(1) model
- Integer-Valued GARCH Process
- The INARCH(1) Model for Overdispersed Time Series of Counts
- The Bias of Autoregressive Coefficient Estimators
- Estimation for an M/G/ queue with incomplete information
- Improved estimation for Poisson INAR(1) models
- Some Limit Theorems for Stationary Processes
- NOTE ON BIAS IN THE ESTIMATION OF AUTOCORRELATION
This page was built for publication: Bias corrections for moment estimators in Poisson INAR(1) and INARCH(1) processes