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• W4283077319 abstract "Variance estimation is important for statistical inference. It becomes nontrivial when observations are masked by serial dependence structures and time-varying mean structures. Existing methods either ignore or sub-optimally handle these nuisance structures. This paper develops a general framework for the estimation of the long-run variance for time series with nonconstant means. The building blocks are difference statistics. The proposed class of estimators is general enough to cover many existing estimators. Necessary and sufficient conditions for consistency are investigated. The first asymptotically optimal estimator is derived. Our proposed estimator is theoretically proven to be invariant to arbitrary mean structures, which may include trends and a possibly divergent number of discontinuities." @default.
• W4283077319 created "2022-06-19" @default.
• W4283077319 creator A5023660252 @default.
• W4283077319 date "2022-06-01" @default.
• W4283077319 modified "2023-09-28" @default.
• W4283077319 title "Optimal difference-based variance estimators in time series: A general framework" @default.
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• W4283077319 doi "https://doi.org/10.1214/21-aos2154" @default.
• W4283077319 hasPublicationYear "2022" @default.
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