Selecting optimal multistep predictors for autoregressive processes of unknown order
نویسندگان
چکیده
منابع مشابه
Selecting Optimal Multistep Predictors for Autoregressive Processes
We consider the problem of choosing the optimal (in the sense of mean-squared prediction error) multistep predictor for an autoregres-sive (AR) process of finite but unknown order. If a working AR model (which is possibly misspecified) is adopted for multistep predictions, then two competing types of multistep predictors (i.e., plug-in and direct predictors) can be obtained from this model. We ...
متن کاملSelecting Optimal Multistep Predictors for Autoregressive Processes of Unknown Order by Ching-kang Ing
We consider the problem of choosing the optimal (in the sense of mean-squared prediction error) multistep predictor for an autoregressive (AR) process of finite but unknown order. If a working AR model (which is possibly misspecified) is adopted for multistep predictions, then two competing types of multistep predictors (i.e., plug-in and direct predictors) can be obtained from this model. We p...
متن کاملMultistep Prediction in Autoregressive Processes
In this paper, two competing types of multistep predictors, i+e+, plug-in and direct predictors, are considered in autoregressive ~AR! processes+When a working model AR~k! is used for the h-step prediction with h . 1, the plug-in predictor is obtained from repeatedly using the fitted ~by least squares! AR~k! model with an unknown future value replaced by their own forecasts, and the direct pred...
متن کاملThe Integration Order of Vector Autoregressive Processes
We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I(2) conditions (Johansen, 1996) is proved and polynomial cointegration discussed in the general setup.
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 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: The Annals of Statistics
سال: 2004
ISSN: 0090-5364
DOI: 10.1214/009053604000000148