This paper investigates convex combinations of long memory estimates from both the original and sub-sampled data. Sub-sampling is carried out by decreasing the sampling rate, which leaves the long memory parameter unchanged. Any convex combination of these sub-sample estimates requires a preliminary correction for the bias observed at lower sampling rates, reported by Souza and Smith (2002). Through Monte Carlo simulations, we investigate the bias and the standard deviation of the combined estimates, as well as the root mean squared error (RMSE). Combining estimates can significantly lower the RMSE of a standard estimator (by about 30% on average for ARFIMA (0, d, 0) series), at the cost of inducing some bias.
Convex combination Long memory Sampling rate
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