scholarly journals VALID EDGEWORTH EXPANSIONS FOR THE WHITTLE MAXIMUM LIKELIHOOD ESTIMATOR FOR STATIONARY LONG-MEMORY GAUSSIAN TIME SERIES

2005 ◽  
Vol 21 (04) ◽  
Author(s):  
Donald W.K. Andrews ◽  
Offer Lieberman
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Long Memory ◽  
Likelihood Estimator ◽  
Edgeworth Expansions ◽  
Gaussian Time ◽  
Gaussian Time Series

scholarly journals ASYMPTOTIC THEORY FOR MAXIMUM LIKELIHOOD ESTIMATION OF THE MEMORY PARAMETER IN STATIONARY GAUSSIAN PROCESSES

2011 ◽  
Vol 28 (2) ◽  
pp. 457-470 ◽  
Author(s):  
Offer Lieberman ◽  
Roy Rosemarin ◽  
Judith Rousseau
Keyword(s):  
Maximum Likelihood ◽  
Long Memory ◽  
Asymptotic Theory ◽  
Likelihood Estimation ◽  
Likelihood Estimator ◽  
Short Memory ◽  
Stationary Gaussian Processes ◽  
Gaussian Time ◽  
Gaussian Time Series ◽  
Memory Parameter

Consistency, asymptotic normality, and efficiency of the maximum likelihood estimator for stationary Gaussian time series were shown to hold in the short memory case by Hannan (1973, Journal of Applied Probability 10, 130–145) and in the long memory case by Dahlhaus (1989, Annals of Statistics 34, 1045–1047). In this paper we extend these results to the entire stationarity region, including the case of antipersistence and noninvertibility.

scholarly journals Random Coefficient Models for Time-Series—Cross-Section Data: Monte Carlo Experiments

2007 ◽  
Vol 15 (2) ◽  
pp. 182-195 ◽  
Author(s):  
Nathaniel Beck ◽  
Jonathan N. Katz
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Cross Section ◽  
Random Coefficient ◽  
Model Parameters ◽  
Cross Section Data ◽  
Likelihood Estimator ◽  
Random Coefficient Models ◽  
Section Data

This article considers random coefficient models (RCMs) for time-series—cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully pooled estimator, the unit by unit (unpooled) estimator, and the (maximum likelihood) RCM estimator. The maximum likelihood estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares estimator of the RCM models is always inferior to the maximum likelihood estimator, and in smaller samples dramatically so.

scholarly journals Maximum likelihood estimation for cooperative sequential adsorption

2009 ◽  
Vol 41 (04) ◽  
pp. 978-1001 ◽  
Author(s):  
Mathew D. Penrose ◽  
Vadim Shcherbakov
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Maximum Likelihood Estimator ◽  
Thermodynamic Limit ◽  
Likelihood Estimation ◽  
Likelihood Estimator ◽  
Current Configuration ◽  
Sequential Adsorption ◽  
Spatial Locations

We consider a model for a time series of spatial locations, in which points are placed sequentially at random into an initially empty region of ℝ d , and given the current configuration of points, the likelihood at location x for the next particle is proportional to a specified function β k of the current number (k) of points within a specified distance of x. We show that the maximum likelihood estimator of the parameters β k (assumed to be zero for k exceeding some fixed threshold) is consistent in the thermodynamic limit where the number of points grows in proportion to the size of the region.

Estimating the memory parameter for potentially non-linear and non-Gaussian time series with wavelets

2022 ◽  
Author(s):  
Chen Xu ◽  
Ye Zhang
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Asymptotic Theory ◽  
Financial Time Series ◽  
Non Linear ◽  
Long Memory Processes ◽  
Non Gaussian ◽  
Gaussian Time ◽  
Gaussian Time Series ◽  
Memory Parameter

Abstract The asymptotic theory for the memory-parameter estimator constructed from the log-regression with wavelets is incomplete for 1/$f$ processes that are not necessarily Gaussian or linear. Having a complete version of this theory is necessary because of the importance of non-Gaussian and non-linear long-memory models in describing financial time series. To bridge this gap, we prove that, under some mild assumptions, a newly designed memory estimator, named LRMW in this paper, is asymptotically consistent. The performances of LRMW in three simulated long-memory processes indicate the efficiency of this new estimator.

scholarly journals Maximum likelihood estimation for cooperative sequential adsorption

2009 ◽  
Vol 41 (4) ◽  
pp. 978-1001 ◽  
Author(s):  
Mathew D. Penrose ◽  
Vadim Shcherbakov
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Maximum Likelihood Estimator ◽  
Thermodynamic Limit ◽  
Likelihood Estimation ◽  
Likelihood Estimator ◽  
Current Configuration ◽  
Sequential Adsorption ◽  
Spatial Locations

We consider a model for a time series of spatial locations, in which points are placed sequentially at random into an initially empty region of ℝd, and given the current configuration of points, the likelihood at location x for the next particle is proportional to a specified function βk of the current number (k) of points within a specified distance of x. We show that the maximum likelihood estimator of the parameters βk (assumed to be zero for k exceeding some fixed threshold) is consistent in the thermodynamic limit where the number of points grows in proportion to the size of the region.

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