Semiparametric Whittle estimation of a cyclical long-memory time series based on generalised exponential models

2016 ◽  
Vol 28 (2) ◽  
pp. 272-295
Author(s):  
Masaki Narukawa
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Exponential Models ◽  
Whittle Estimation ◽  
Memory Time

EXACT LOCAL WHITTLE ESTIMATION IN LONG MEMORY TIME SERIES WITH MULTIPLE POLES

2020 ◽  
Vol 36 (6) ◽  
pp. 1064-1098 ◽  
Author(s):  
Josu Arteche
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Unit Roots ◽  
Finite Sample ◽  
Production Index ◽  
Whittle Estimation ◽  
Memory Time ◽  
Local Whittle Estimator ◽  
Consistency And Asymptotic Normality ◽  
Multiple Poles

A generalization of the Exact Local Whittle estimator in Shimotsu and Phillips (2005, Annals of Statistics 33, 1890–1933) is proposed for jointly estimating all the memory parameters in general long memory time series that possibly display standard, seasonal, and/or other cyclical strong persistence. Consistency and asymptotic normality are proven for stationary, nonstationary, and noninvertible series, permitting straightforward standard inference of interesting hypotheses such as the existence of unit roots and equality of memory parameters at some or all seasonal frequencies, which can be used as a prior test for the application of seasonal differencing filters. The effects of unknown deterministic terms are also discussed. Finally, the finite sample performance is analyzed in an extensive Monte Carlo exercise and an application to an U.S. Industrial Production index.

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.

Sign in / Sign up

Export Citation Format

Share Document