Non-stationary Gaussian ARFIMA processes: Estimation and application
نویسندگان
چکیده
منابع مشابه
Minimum distance estimation of stationary and non-stationary ARFIMA processes
A new parametric minimum distance time-domain estimator for ARFIMA processes is introduced in this paper. The proposed estimator minimizes the sum of squared correlations of residuals obtained after filtering a series through ARFIMA parameters. The estimator is easy to compute and is consistent and asymptotically normally distributed for fractionally integrated (FI) processes with an integratio...
متن کاملMaximum Likelihood Estimation of Stationary Multivariate ARFIMA Processes
This paper considers the maximum likelihood estimation (MLE) of a class of stationary and invertible vector autoregressive fractionally integrated moving-average (VARFIMA) processes considered in (26) of Luceño [1] or Model A of Lobato [2] where each component yi,t is a fractionally integrated process of order di, i = 1, . . . , r. Under the conditions outlined in Assumption 1 of this paper, th...
متن کاملEmpirical Likelihood Approach for Non-Gaussian Vector Stationary Processes and Its Application to Minimum Contrast Estimation
A. For a class of vector-valued non-Gaussian stationary processes with unknown parameters, we develop the empirical likelihood approach. In time series analysis it is known that Whittle likelihood is one of the most fundamental tools to get a good estimator of unknown parameters, and that the score functions are asymptotically normal. Motivated by the Whittle likelihood, we apply the emp...
متن کاملBivariate Simulation of Non Stationary and Non Gaussian Observed Processes Application to Sea State Parameters
A method for arti®cially generating operational sea state histories has been developed. This is a distribution free method to simulate bivariate non stationary and non Gaussian random processes. This method is applied to the simulation of the bivariate process (H s , T p) of sea state parameters. The time series respects the physical constraints existing between the signi®cant wave height and t...
متن کاملA translation model for non-stationary, non-Gaussian random processes
A model for simulation of non-stationary, non-Gaussian processes based on non-linear translation of Gaussian random vectors is presented. This method is a generalization of traditional translation processes that includes the capability of simulating samples with spatially or temporally varying marginal probability density functions. A formal development of the properties of the resulting proces...
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: Brazilian Review of Econometrics
سال: 2002
ISSN: 1980-2447
DOI: 10.12660/bre.v22n12002.2746