On parameter estimation for locally stationary long-memory processes

@article{Beran2009OnPE,
  title={On parameter estimation for locally stationary long-memory processes},
  author={Jan Beran},
  journal={Journal of Statistical Planning and Inference},
  year={2009},
  volume={139},
  pages={900-915}
}
  • J. Beran
  • Published 1 March 2009
  • Mathematics
  • Journal of Statistical Planning and Inference
View via Publisher
kops.uni-konstanz.de
34 Citations
Highly Influential Citations
10
Background Citations
16
Methods Citations
8
Results Citations
3

34 Citations

Time varying long memory parameter estimation for locally stationary long memory processes
  • Lihong Wang
  • Mathematics
    Communications in Statistics - Theory and Methods
  • 2018
Abstract The semiparametric estimators of time varying long memory parameter are investigated for locally stationary long memory processes. The GPH estimator and the local Whittle estimator are…
On the Estimation of Locally Stationary Long-Memory Processes
TLDR
A central limit theorem for the Whittle likelihood estimation method is derived under mild distributional conditions, extending its application to a wide range of non-Gaussian time series.
AN EFFICIENT ESTIMATOR FOR LOCALLY STATIONARY GAUSSIAN LONG-MEMORY PROCESSES
This paper addresses the estimation of locally stationary long-range dependent processes, a methodology that allows the statistical analysis of time series data exhibiting both nonstationarity and…
0472 1 ON THE ESTIMATION OF LOCALLY STATIONARY LONG-MEMORY PROCESSES
This paper establishes the statistical properties of a spectrum-based Whittle parameter estimation procedure for long-range dependent locally stationary processes. Both theoretical and empirical…
On the sample mean of locally stationary long-memory processes
Locally stationary long memory estimation
Estimation of slowly time-varying trend function in long memory regression models
ABSTRACT We study the asymptotic properties of the least-squares estimator for the trend function of a particular class of locally stationary models, which are defined by considering a smooth…
Simultaneous quantile inference for non-stationary long-memory time series
We consider the simultaneous or functional inference of time-varying quantile curves for a class of non-stationary long-memory time series. New uniform Bahadur representations and Gaussian…
On estimating the marginal distribution of a detrended series with long memory
ABSTRACT Consider continuous real-valued time series observations yj, j = 1, 2, …, n, with finite variance and a smooth trend. Let be the residuals which have a cumulative distribution function F(tj,…
Measuring stationarity in long-memory processes
In this paper we consider the problem of measuring stationarity in locally stationary long-memory processes. We introduce an $L_2$-distance between the spectral density of the locally stationary…
...
...

References

SHOWING 1-10 OF 70 REFERENCES
ON ESTIMATION OF LONG-MEMORY TIME SERIES MODELS
Summary This paper discusses estimation associated with the long-memory time series models proposed by Granger & Joyeux (1980) and Hosking (1981). We consider the maximum likelihood estimator and…
On recursive estimation for time varying autoregressive processes
This paper focuses on recursive estimation of time varying autoregressive processes in a nonparametric setting. The stability of the model is revisited and uniform results are provided when the…
ESTIMATION OF THE LONG‐MEMORY PARAMETER, BASED ON A MULTIVARIATE CENTRAL LIMIT THEOREM
. Long memory is known to occur in many fields of statistical application. Stationary processes whose correlations decay asymptotically like ‖k‖2H-2, where k is the lag and He (0.5, 1), provide…
On the Optimal Segment Length for Parameter Estimates for Locally Stationary Time Series
We discuss the behaviour of parameter estimates when stationary time series models are fitted locally to non‐stationary processes which have an evolutionary spectral representation. A particular…
Wavelet estimation of a local long memory parameter
There are a number of estimators of a long-memory process’ long-memory parameter when the parameter is assumed to hold constant over the entire data set, but currently no estimator exists for a…
On unified model selection for stationary and nonstationary short- and long-memory autoregressive processes
SUMMARY The question of model choice for the class of stationary and nonstationary, fractional and nonfractional autoregressive processes is considered. This class is defined by the property that the…
The Fitting of Non-stationary Time-series Models with Time-dependent Parameters
SUMMARY We use the method of weighted least squares to estimate the timedependent parameters of non-stationary time-series models. Approximate expressions for the bias and variance of the estimates,…
Asymptotic self‐similarity and wavelet estimation for long‐range dependent fractional autoregressive integrated moving average time series with stable innovations
Abstract.  Methods for parameter estimation in the presence of long‐range dependence and heavy tails are scarce. Fractional autoregressive integrated moving average (FARIMA) time series for positive…
Statistics for long-memory processes
TLDR
Theorems of Stationary Processes with Long Memory Limit Theorems and Estimations of Long Memory-Heuristic Approaches, Forecasting Regression Goodness of Fit Tests, and Robust Estimation of Long memory estimates are presented.
An introduction to long-memory time series models and fractional differencing
TLDR
Generation and estimation of models that provide potentially useful long-memory forecasting properties and applications on generated and real data presented.
...
...