A Unified View of Signal Extraction, Benchmarking, Interpolation and Extrapolation of Time Series |
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Authors: | Estela Bee Dagum Pierre A Cholette Zhao-Guo Chen |
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Institution: | University of Bologna, Faculty of Statistical Sciences, Via delle Belle Arti 41, (40126) Bologna, Italy.;Time Series Research and Analysis Centre, Statistics Canada, Ottawa, Canada KIA 0T6 |
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Abstract: | 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. |
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Keywords: | Dynamic stochastic regression Generalized least squares Minimum variance linear unbiased estimators Signal extraction Benchmarking Interpolation Extrapolation ARIMA modeling Heteroscedastic error |
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