dCovTS-package: Distance Covariance and Correlation Theory for Time Series

In dCovTS: Distance Covariance and Correlation for Time Series Analysis

Distance Covariance and Correlation Theory for Time Series

Description

Computing and plotting the distance covariance and correlation function of a univariate or a multivariate time series. Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included.

Details

Package:	dCovTS
Type:	Package
Version:	1.4
Date:	2023-09-28
License:	GPL(>=2)

Note

Disclaimer: Dr Maria Pitsillou is the actual creator of this package. Dr Tsagris is the current maintainer.

Improvements: We have modified the codes to run faster, we included the packages Rfast and Rfast2 for fast computations and the "dcov" package that allows for extremely fast computations of the distance correlation/covariance with univariate data.

Author(s)

Michail Tsagris, Maria Pitsillou and Konstantinos Fokianos.

References

Edelmann, D, K. Fokianos. and M. Pitsillou. (2019). An Updated Literature Review of Distance Correlation and Its Applications to Time Series. International Statistical Review, 87, 237-262.

Fokianos, K. and M. Pitsillou. (2018). Testing independence for multivariate time series via the auto-distance correlation matrix. Biometrika, 105, 337-352.

Fokianos, K. and M. Pitsillou. (2017). Consistent testing for pairwise dependence in time series. Technometrics, 159, 262-3270.

Dehling, H. and T. Mikosch. (1994). Random quadratic forms and the bootstrap for U-statistics. Journal of Multivariate Analysis 51, 392-413.

Hong, Y. (1999). Hypothesis testing in time series via the empirical characteristic function: A generalized spectral density approach. Journal of the American Statistical Association, 94, 1201-1220.

Hong, Y. (1996). Consistent testing for serial correlation of unknown form. Econometrica, 64, 837-864.

Huo, X. and G. J. Szekely. (2016). Fast Computing for Distance Covariance. Technometrics, 58, 435-447.

Leucht, A. and M. H. Neumann (2013). Dependent wild bootstrap for degenerate U- and V- statistics. Journal of Multivariate Analysis, 117, 257-280.

Pitsillou, M. and K. Fokianos. (2016). dCovTS: Distance Covariance/Correlation for Time Series. R Journal, 8, 324-340.

Politis, N. P., J. P. Romano and M. Wolf (1999). Subsampling. New York: Springer.

Shao, X. (2010). The dependent wild bootstrap. Journal of the American Statistical Association, 105, 218-235.

Shumway, R. H. and D. S. Stoffer (2011). Time Series Analysis and Its Applications With R Examples. New York: Springer. Third Edition.

Szekely, G. J. and M. L. Rizzo (2014). Partial distance correlation with methods for dissimilarities. The Annals of Statistics, 42, 2382-2412.

Szekely, G. J., M. L. Rizzo and N. K. Bakirov (2007). Measuring and testing dependence by correlation of distances. The Annals of Statistics, 35, 2769-2794, .

Tsay, R. S. (2014). Multivariate Time Series Analysis with R and Financial Applications. Hoboken, NJ: Wiley.

Tsay, R. S. (2010). Analysis of Financial Time Series. Hoboken, NJ: Wiley. Third edition.

Zhou, Z. (2012). Measuring nonlinear dependence in time series, a distance correlation approach. Journal of Time Series Analysis, 33, 438-457.

dCovTS documentation built on Sept. 29, 2023, 1:06 a.m.