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Abstract
This article introduces a nonparametric method to estimate the covariance matrix for the stationary part of the signal (hidden in data), to enable benchmarking via signal extraction. Some discussions and simulations are carried out to compare the proposed benchmarking method to the regression method development by Cholette and Dagum and the signal extraction method developed by Hillmer and Trabelsi suggesting autoregression integrated moving average (ARIMA) models for the signal. The results show that the nonparametric method is feasible, robust, and almost as efficient as the signal extraction method when the true model for the signal is known.