dCovTS-package: Distance Covariance and Correlation theory in time series

Description

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

Arguments

Details

Package:

dCovTS

Type:

Package

Version:

1.0

Date:

2016-03-08

License:

GPL(>=2)

References

Fokianos K. and M. Pitsillou (2016a). Consistent testing for pairwise dependence in time series. Technometrics, http://dx.doi.org/10.1080/00401706.2016.1156024.

Fokianos K. and M. Pitsillou (2016b). On multivariate auto-distance covariance and correlation functions. Submitted for publication.

Hong, Y. (1996). Consistent testing for serial correlation of unknown form. Econometrica $\textbf{64}$, 837-864, http://dx.doi.org/10.2307/2171847.

Hong, Y. (1999). Hypothesis testing in time series via the empirical characteristic function: A generalized spectral density approach. Journal of the American Statistical Association $\textbf{94}$, 1201-1220, http://dx.doi.org/10.1080/01621459.1999.10473874.

Leucht, A. and M. H. Neumann (2013). Dependent wild bootstrap for degenerate U- and V- statistics. Journal of Multivariate Analysis $\textbf{117}$, 257-280, http://dx.doi.org/10.1016/j.jmva.2013.03.003.

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 $\textbf{105}$, 218-235, http://dx.doi.org/10.1198/jasa.2009.tm08744.

Shumway, R. H. and D. S. Stoffer (2011). Time Series Analysis and Its Applications With R Examples. New York: Springer. Third Edition. http://www.stat.pitt.edu/stoffer/tsa3/

Szekely, G. J., M. L. Rizzo and N. K. Bakirov (2007). Measuring and testing dependence by correlation of distances. The Annals of Statistics $\textbf{35}$, 2769-2794, http://dx.doi.org/10.1214/009053607000000505.

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

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

Zhou, Z. (2012). Measuring nonlinear dependence in time series, a distance correlation approach. Journal of Time Series Analysis $\textbf{33}$, 438-457, http://dx.doi.org/10.1111/j.1467-9892.2011.00780.x.