On Bayesian Identification of Autoregressive Processes
نویسندگان
چکیده
منابع مشابه
Bayesian Model Selection for Beta Autoregressive Processes
We deal with Bayesian inference for Beta autoregressive processes. We restrict our attention to the class of conditionally linear processes. These processes are particularly suitable for forecasting purposes, but are difficult to estimate due to the constraints on the parameter space. We provide a full Bayesian approach to the estimation and include the parameter restrictions in the inference p...
متن کاملOn the existence of Hilbert valued periodically correlated autoregressive processes
In this paper we provide sufficient condition for existence of a unique Hilbert valued ($mathbb{H}$-valued) periodically correlated solution to the first order autoregressive model $X_{n}=rho _{n}X_{n-1}+Z_{n}$, for $nin mathbb{Z}$, and formulate the existing solution and its autocovariance operator. Also we specially investigate equivalent condition for the coordinate process...
متن کاملModel Identification for Infinite Variance Autoregressive Processes
We consider model identification for infinite variance autoregressive time series processes. It is shown that a consistent estimate of autoregressive model order can be obtained by minimizing Akaike’s information criterion, and we use all-pass models to identify noncausal autoregressive processes and estimate the order of noncausality (the number of roots of the autoregressive polynomial inside...
متن کاملBayesian analysis of autoregressive moving average processes with unknown orders
A Bayesian model selection for modelling a time series by an autoregressive–moving–average model (ARMA) is presented. The posterior distribution of unknown parameters and the selected orders are obtained by the Markov chain Monte Carlo (MCMC) method. An MCMC algorithm that represents the parameters of the model as a point process has been implemented. The method is illustrated on simulated seri...
متن کاملBayesian inference of time varying parameters in autoregressive processes
In the autoregressive process of first order AR(1), a homogeneous correlated time series ut is recursively constructed as ut = q ut−1 + σ t, using random Gaussian deviates t and fixed values for the correlation coefficient q and for the noise amplitude σ. To model temporally heterogeneous time series, the coefficients qt and σt can be regarded as time-dependent variables by themselves, leading ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pakistan Journal of Statistics and Operation Research
سال: 2015
ISSN: 2220-5810,1816-2711
DOI: 10.18187/pjsor.v11i1.709