Change Point Detection with Multivariate Observations Based on Characteristic Functions
We consider break-detection procedures for vector observations, both under independence as well as under an underlying structural time series scenario. The new methods involve L2-type criteria based on empirical characteristic functions. Asymptotic as well as Monte-Carlo results are presented. The new methods are also applied to time-series data from the financial sector.
The research of Simos Meintanis was partially supported by grant number 11699 of the Special Account for Research Grants (E\(\Lambda \)KE) of the National and Kapodistrian University of Athens. The research of Marie Hušková and Zdeněk Hlávka was partially supported by grant GAČR 15-09663S and AP research network grant Nr. P7/06 of the Belgian government (Belgian Science Policy).
- Bai, J.: Vector autoregressions with structural changes in regression coefficients and in variance–covariance matrices. Ann. Econom. Financ. 1, 303–339 (2000)Google Scholar
- Dvořák. M.: Stability in autoregressive time series models. PhD. Thesis, Charles University in Prague, Czech Republic (2015)Google Scholar
- Dvořák, M.: Darling–Erdős type test for change detection in stationary VAR models. Commun. Statist. – Theor. Meth. (2016) doi: 10.1080/03610926.2014.995828
- Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F., Hothorn, T.: mvtnorm: Multivariate Normal and t Distributions. R package version 1.0-0 (2014) http://CRAN.R-project.org/package=mvtnorm
- Genz, A., Bretz, F.: Computation of Multivariate Normal and t Probabilities. Lecture Notes in Statistics, Vol. 195. Springer-Verlag, Heidelberg (2009)Google Scholar
- Hlávka, Z., Hušková, M., Kirch, C., Meintanis, S.G.: Bootstrap procedures for online monitoring of changes in autoregressive models. Commun. Statist. – Simul. Comput. 45, 2471–2490 (2016)Google Scholar
- Hlávka, Z., Hušková, M., Kirch, C., Meintanis, S.G.: Fourier-type tests involving martingale difference processes. Econometr. Rev. 36, 468–492 (2017)Google Scholar
- Lütkepohl, H.: Identifying structural vector autoregressions via changes in volatility. Universität Berlin, Berlin (2012)Google Scholar
- Siegfried, N.A.: An information-theoretic extension to structural VAR modelling. Hamburg University, Dept. Economics, Working Paper 02–03 (2002)Google Scholar