scholarly journals Estimation of long-range dependence in gappy Gaussian time series

2019 ◽  
Vol 30 (1) ◽  
pp. 167-185
Author(s):  
Peter F. Craigmile ◽  
Debashis Mondal
Keyword(s):  
Time Series ◽  
Long Range ◽  
Long Range Dependence ◽  
Gaussian Time ◽  
Gaussian Time Series

scholarly journals NON-GAUSSIAN LOG-PERIODOGRAM REGRESSION

2000 ◽  
Vol 16 (1) ◽  
pp. 44-79 ◽  
Author(s):  
Carlos Velasco
Keyword(s):  
Time Series ◽  
Long Range ◽  
Asymptotic Properties ◽  
Asymptotic Results ◽  
Regression Estimate ◽  
Finite Samples ◽  
Non Gaussian ◽  
Periodogram Estimate ◽  
Gaussian Time ◽  
Gaussian Time Series

We show the consistency of the log-periodogram regression estimate of the long memory parameter for long range dependent linear, not necessarily Gaussian, time series when we make a pooling of periodogram ordinates. Then, we study the asymptotic behavior of the tapered periodogram of long range dependent time series for frequencies near the origin, and we obtain the asymptotic distribution of the log-periodogram estimate for possibly non-Gaussian observation when the tapered periodogram is used. For these results we rely on higher order asymptotic properties of a vector of periodogram ordinates of the linear innovations. Finally, we assess the validity of the asymptotic results for finite samples via Monte Carlo simulation.

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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