A consistent estimator for skewness of partial sums of dependent data

@article{Nasari2020ACE,
  title={A consistent estimator for skewness of partial sums of dependent data},
  author={Masoud M. Nasari and Mohamedou Ould-Haye},
  journal={arXiv: Statistics Theory},
  year={2020}
}
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References

SHOWING 1-10 OF 10 REFERENCES

Two estimators of the long-run variance: beyond short memory

This paper deals with the estimation of the long-run variance of a stationary sequence. We extend the usual Bartlett-kernel heteroskedasticity and autocorrelation consistent (HAC) estimator to deal

Tests for Skewness, Kurtosis, and Normality for Time Series Data

We present the sampling distributions for the coefficient of skewness, kurtosis, and a joint test of normality for time series observations. We show that when the data are serially correlated,

Limit theorems for functionals of moving averages

Let X n = Σ i=1 ∞ a i e n-1, where the e i are i.i.d. with mean 0 and finite second moment and the a i are either summable or regularly varying with index ∈ (-1, -1/2). The sequence {X n } has short

Looking for Skewness in Financial Time Series

In this paper, we study marginal and conditional skewness in financial returns for nine time series of major international stock indices. For this purpose, we develop a new variant of the GARCH model

Fractional ARIMA with stable innovations

Large Sample Inference for Long Memory Processes

Introduction Estimation Some Inference Problems Residual Empirical Processes Regression Models Nonparametric Regression with Heteroscedastic Errors Model Checking under Long Memory Long Memory under

Two estimators for the long-run variance: Beyound short memory.Journal of Econometrics

  • 2009

The Invariance Principle for Stationary Processes