Abstract
A new kernel for estimating long-run variances of stationary seasonal time series is proposed. The proposed kernel has an oscillating pattern which is in harmony with that of the autocovariance functions of seasonal time series. A Monte-Carlo experiment shows that the estimator based on the proposed kernel outperforms estimators based on existing kernels such as the Bartlett kernel, Parzen kernel, and Tukey–Hanning kernel for two typical monthly time series processes with moderate autocorrelations.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
