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A Unified View of Signal Extraction, Benchmarking, Interpolation and Extrapolation of Time Series
Author(s) -
Dagum Estela Bee,
Cholette Pierre A.,
Chen ZhaoGuo
Publication year - 1998
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/j.1751-5823.1998.tb00372.x
Subject(s) - heteroscedasticity , extrapolation , interpolation (computer graphics) , estimator , smoothing , autocorrelation , series (stratigraphy) , statistics , computer science , mathematics , time series , algorithm , econometrics , artificial intelligence , motion (physics) , paleontology , biology
Summary Time series data are often subject to statistical adjustments needed to increase accuracy, replace missing values and/or facilitate data analysis. The most common adjustments made to original observations are signal extraction (e.g. smoothing), benchmarking, interpolation and extrapolation. In this article, we present a general dynamic stochastic regression model, from which most of these adjustments can be performed, and prove that the resulting generalized least square estimator is minimum variance linear unbiased. We extend current methods to include those cases where the signal follows a mixed model (deterministic and stochastic components) and the errors are autocorrelated and heteroscedastic.