### Table 5 illustrates the long term memory structure

1991

### Table 4. Fitted Parameter Values for Various Score Generators Spline-Long Memory-Asymmetric Semiparametric Nonlinear

1996

"... In PAGE 19: ... In the stochastic volatility models that passed the chi-square test under the zeta preferred score generator, we nd that introducing the asymmetric leverage e ect decreases the chi-square statistic by more than 2, and adding the long-memory decreases the chi-square statistic by more than 8.10 In Table4 , we present the tted parameter values of the stochastic volatility model with the spline error transformation, long memory and the asymmetric leverage e ect for various score generators. While the parameter estimates for the GHT selected scores are quite consistent with each other, they are somewhat di erent from the estimates for the zeta preferred model.... ..."

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### Table 1: Old episodes in the long-term memory of the system.

### Table 7: Bayesian estimation of a long-memory stochastic volatility model for Aluminum

"... In PAGE 29: ... Thus, we only report the results from our Bayesian estimator using the Uniform prior for the AA stock return data. In Table7 we report the same summary statistics from the MCMC simulator and the GPH estimator for the volatility of AA daily returns as we did in the previous simulations. In addition, we also plot in Fig.... ..."

### Table 4: Average estimated long-memory parameter of some transformations of 2,000 simulated break processes with break probability p.

"... In PAGE 22: ...Table 4: Average estimated long-memory parameter of some transformations of 2,000 simulated break processes with break probability p. Table4 shows estimates of the long-memory parameter of various transformations of four break processes with break probabilities p = 0.... In PAGE 23: ... Table4 demonstrates that the estimated long-memory parameter decreases only slightly with the order of the polynomial transformation. In particular, this estimate seems to be independent of the Hermite rank of the transformation.... ..."

### Table 1: Moderate long memory averaged periodogram biases Monte Carlo BIASES for the averaged periodogram estimate of long memory applied to an ARFIMA(0, :2, 0) series with ve speci ed innovation structures.n=64 n=128 n=256

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### Table 2: Moderate long memory averaged periodogram RMSEs Monte Carlo ROOT MEAN SQUARED ERRORS for the averaged periodogram estimate of long memory applied to an ARFIMA(0, :2, 0) series with ve speci ed innovation structures. n=64 n=128 n=256

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