Estimation for autoregressive processes with positive innovations
نویسندگان
چکیده
منابع مشابه
Consistent estimation and order selection for non-stationary autoregressive processes with stable innovations
A possibly non-stationary autoregressive process, of unknown finite order, with possibly infinite-variance innovations is studied. The Ordinary Least Squares autoregressive parameter estimates are shown to be consistent, and their rate of convergence, which depends on the index of stability, α, is established. We also establish consistency of lag-order selection criteria in the non-stationary c...
متن کاملRecurrence Properties of Autoregressive Processes With Super-Heavy Tailed Innovations
This paper studies recurrence properties of autoregressive (AR) processes with “super-heavy tailed” innovations. Specifically, we study the case where the innovations are distributed, roughly speaking, as log-Pareto random variables (i.e., the tail decay is essentially a logarithm raised to some power). We show that these processes exhibit interesting and somewhat surprising behavior. In partic...
متن کاملEstimation of autoregressive models with epsilon-skew-normal innovations
A non-Gaussian autoregressive model with epsilon-skew-normal innovations is introduced. Moments and maximum likelihood estimators of the parameters are proposed and their limit distributions are derived. Monte Carlo simulation results are analysed and the model is fitted to a real time series. © 2009 Elsevier Inc. All rights reserved.
متن کاملPredictor selection for positive autoregressive processes
Let observations y1, · · · , yn be generated from a first-order autoregressive (AR) model with positive errors. In both the stationary and unit root cases, we derive moment bounds and limiting distributions of an extreme value estimator, ρ̂n, of the AR coefficient. These results enable us to provide asymptotic expressions for the mean squared error (MSE) of ρ̂n and the mean squared prediction err...
متن کاملNonparametric Estimation of Volatility Models with General Autoregressive Innovations
We are interested in modeling a zero mean heteroscedastic time series process with autoregressive error process of finite known order p. The model can be used for testing a martingale difference sequence hypothesis that is often adopted uncritically in financial time series against a fairly general alternative. When the argument is deterministic, we provide an innovative nonparametric estimator...
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: Communications in Statistics. Stochastic Models
سال: 1992
ISSN: 0882-0287
DOI: 10.1080/15326349208807235