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Asymptotics for Autocovariances and Integrated Periodograms for Linear Processes Observed at Lower Frequencies
Author(s) -
Niebuhr Tobias,
Kreiss JensPeter
Publication year - 2014
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12019
Subject(s) - mathematics , autoregressive model , limit (mathematics) , central limit theorem , autocovariance , statistics , sample size determination , sample (material) , series (stratigraphy) , statistical physics , linear model , econometrics , mathematical analysis , physics , paleontology , fourier transform , biology , thermodynamics
Summary One of the most frequently used class of processes in time series analysis is the one of linear processes. For many statistical quantities, among them sample autocovariances and sample autocorrelations, central limit theorems are available in the literature. We investigate classical linear processes under a nonstandard observation pattern; namely, we assume that we are only able to observe the linear process at a lower frequency. It is shown that such observation pattern destroys the linear structure of the observations and leads to substantially different asymptotic results for standard statistical quantities. Central limit theorems are given for sample autocovariances and sample autocorrelations as well as more general integrated periodograms and ratio statistics. Moreover, for specific autoregressive processes, the possibilities to estimate the parameters of the underlying autoregression from lower frequency observations are addressed. Finally, we suggest for autoregressions of order 2 a valid bootstrap procedure. A small simulation study demonstrates the performance of the bootstrap proposal for finite sample size.