scholarly journals Spatial long memory

2019 ◽  
Vol 3 (1) ◽  
pp. 243-256
Author(s):  
Peter M. Robinson
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Spatial Data ◽  
Long Memory ◽  
The Other ◽  
Future Prospects ◽  
Memory Time ◽  
Short Memory ◽  
The One ◽  
Relevant Work

AbstractWe discuss developments and future prospects for statistical modeling and inference for spatial data that have long memory. While a number of contributons have been made, the literature is relatively small and scattered, compared to the literatures on long memory time series on the one hand, and spatial data with short memory on the other. Thus, over several topics, our discussions frequently begin by surveying relevant work in these areas that might be extended in a long memory spatial setting.

scholarly journals A GENERALIZED PORTMANTEAU TEST FOR INDEPENDENCE BETWEEN TWO STATIONARY TIME SERIES

2009 ◽  
Vol 25 (1) ◽  
pp. 195-210 ◽  
Author(s):  
Xiaofeng Shao
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Goodness Of Fit ◽  
Stationary Time Series ◽  
Test Statistics ◽  
Goodness Of Fit Test ◽  
Stationary Time ◽  
Standard Normal ◽  
Short Memory ◽  
The One

We propose generalized portmanteau-type test statistics in the frequency domain to test independence between two stationary time series. The test statistics are formed analogous to the one in the paper by Chen and Deo (2004, Econometric Theory 20, 382–416), who extended the applicability of the portmanteau goodness-of-fit test to the long memory case. Under the null hypothesis of independence, the asymptotic standard normal distributions of the proposed statistics are derived under fairly mild conditions. In particular, each time series is allowed to possess short memory, long memory, or antipersistence. A simulation study shows that the tests have reasonable size and power properties.

Quantile spectral analysis and long-memory time series

1986 ◽  
Vol 23 (A) ◽  
pp. 41-54 ◽  
Author(s):  
Emanuel Parzen
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Long Memory ◽  
Model Identification ◽  
Time Series Model ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Memory Time ◽  
Short Memory ◽  
Simultaneous Use

An approach to time series model identification is described which involves the simultaneous use of frequency, time and quantile domain algorithms; the approach is called quantile spectral analysis. It proposes a framework to integrate the analysis of long-memory (non-stationary) time series with the analysis of short-memory (stationary) time series.

Quantile spectral analysis and long-memory time series

1986 ◽  
Vol 23 (A) ◽  
pp. 41-54 ◽  
Author(s):  
Emanuel Parzen
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Long Memory ◽  
Model Identification ◽  
Time Series Model ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Memory Time ◽  
Short Memory ◽  
Simultaneous Use

An approach to time series model identification is described which involves the simultaneous use of frequency, time and quantile domain algorithms; the approach is called quantile spectral analysis. It proposes a framework to integrate the analysis of long-memory (non-stationary) time series with the analysis of short-memory (stationary) time series.

scholarly journals FIXED-B ASYMPTOTICS FOR THE STUDENTIZED MEAN FROM TIME SERIES WITH SHORT, LONG, OR NEGATIVE MEMORY

2011 ◽  
Vol 28 (2) ◽  
pp. 471-481 ◽  
Author(s):  
Tucker McElroy ◽  
Dimitris N. Politis
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Long Memory ◽  
Variance Estimation ◽  
Simulation Studies ◽  
Sample Mean ◽  
Memory Time ◽  
Negative Memory ◽  
Short Memory ◽  
The Mean

This paper considers the problem of variance estimation for the sample mean in the context of long memory and negative memory time series dynamics, adopting the fixed-bandwidth approach now popular in the econometrics literature. The distribution theory generalizes the short memory results of Kiefer and Vogelsang (2005, Econometric Theory 21, 1130–1164). In particular, our results highlight the dependence on the kernel (we include flat-top kernels), whether or not the kernel is nonzero at the boundary, and, most important, whether or not the process is short memory. Simulation studies support the importance of accounting for memory in the construction of confidence intervals for the mean.

A New Test for Short Memory in Long Memory Time Series

2015 ◽  
Vol 69 (3) ◽  
pp. 182-190
Author(s):  
Timothy A. C. Hughes ◽  
Jaechoul Lee
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Memory Time ◽  
Short Memory

scholarly journals On Mixture GARCH Models: Long, Short Memory and Application in Finance

2021 ◽  
Vol 2 (2) ◽  
pp. 01-07
Author(s):  
Halim Zeghdoudi ◽  
Madjda Amrani
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Stationary Solution ◽  
Long Memory ◽  
Nonlinear Time Series ◽  
The Other ◽  
Garch Models ◽  
Empirical Application ◽  
Monte Carlo Experiments ◽  
Short Memory

In this work, we study the famous model of volatility; called model of conditional heteroscedastic autoregressive with mixed memory MMGARCH for modeling nonlinear time series. The MMGARCH model has two mixing components, one is a GARCH short memory and the other is GARCH long memory. the main objective of this search for finds the best model between mixtures of the models we made (long memory with long memory, short memory with short memory and short memory with long memory) Also, the existence of its stationary solution is discussed. The Monte Carlo experiments demonstrate we discovered theoretical. In addition, the empirical application of the MMGARCH model (1, 1) to the daily index DOW and NASDAQ illustrates its capabilities; we find that for the mixture between APARCH and EGARCH is superior to any other model tested because it produces the smallest errors.

On the estimation of short memory components in long memory time series models

2016 ◽  
Vol 20 (4) ◽  
Author(s):  
Richard T. Baillie ◽  
George Kapetanios
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Models ◽  
Memory Process ◽  
Long Memory Process ◽  
Fractional Differencing ◽  
Memory Time ◽  
Short Memory ◽  
Short Memory Process ◽  
Memory Parameter

AbstractA substantial amount of recent time series research has emphasized semi-parameteric estimators of a long memory parameter and we provide a selective review of the literature on this issue. We consider such estimators applied to the issue of estimating the parameters relating to a short memory process which is embedded within the long memory process. We consider the fractional differencing filter and the subsequent properties of a two step estimator of the short memory parameters. We conclude that while the semi-parametric estimators can have excellent properties in terms of estimating the long memory parameter, they do not have good properties when applied to the two step estimator of short memory

scholarly journals A harmonically weighted filter for cyclical long memory processes

2021 ◽  
Author(s):  
Federico Maddanu
Keyword(s):  
Spectral Density ◽  
Long Memory ◽  
Convergence In Probability ◽  
Simulation Methods ◽  
Integrated Process ◽  
Paleoclimatic Data ◽  
Memory Time ◽  
Long Memory Processes ◽  
Short Memory ◽  
Memory Parameter

AbstractThe estimation of the long memory parameter d is a widely discussed issue in the literature. The harmonically weighted (HW) process was recently introduced for long memory time series with an unbounded spectral density at the origin. In contrast to the most famous fractionally integrated process, the HW approach does not require the estimation of the d parameter, but it may be just as able to capture long memory as the fractionally integrated model, if the sample size is not too large. Our contribution is a generalization of the HW model, denominated the Generalized harmonically weighted (GHW) process, which allows for an unbounded spectral density at $$k \ge 1$$ k ≥ 1 frequencies away from the origin. The convergence in probability of the Whittle estimator is provided for the GHW process, along with a discussion on simulation methods. Fit and forecast performances are evaluated via an empirical application on paleoclimatic data. Our main conclusion is that the above generalization is able to model long memory, as well as its classical competitor, the fractionally differenced Gegenbauer process, does. In addition, the GHW process does not require the estimation of the memory parameter, simplifying the issue of how to disentangle long memory from a (moderately persistent) short memory component. This leads to a clear advantage of our formulation over the fractional long memory approach.

scholarly journals On nonparametric regression for bivariate circular long-memory time series

2021 ◽  
Author(s):  
Jan Beran ◽  
Britta Steffens ◽  
Sucharita Ghosh
Keyword(s):  
Time Series ◽  
Nonparametric Regression ◽  
Long Range ◽  
Long Memory ◽  
Regression Function ◽  
Confidence Bands ◽  
Long Range Dependence ◽  
Asymptotic Results ◽  
Memory Time ◽  
Circular Time Series

AbstractWe consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.

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