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Articles

A computational bootstrap procedure to compare two dependent time series

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Pages 2831-2847 | Received 23 Jan 2019, Accepted 01 Jul 2019, Published online: 09 Jul 2019

References

  • Coates DS, Diggle P. Tests for comparing two estimated spectral densities. J Time Ser Anal. 1986;7:7–20. doi: 10.1111/j.1467-9892.1986.tb00482.x
  • Piccolo D. A distance measure for classifying arima models. J Time Ser Anal. 1990;11:153–164. doi: 10.1111/j.1467-9892.1990.tb00048.x
  • Caiado J, Crato N, Pena D. A periodogram-based metric for time series classification. Comput Statist Data Anal. 2006;50:2668–2684. doi: 10.1016/j.csda.2005.04.012
  • Jin L, Wang S. A new test for checking the equality of the correlation structures of two time series. J Time Ser Anal. 2016;37:355–368. doi: 10.1111/jtsa.12162
  • Grant AJ, Quinn BG. Parametric spectral discrimination. J Time Ser Anal. 2017;38:838–864. doi: 10.1111/jtsa.12238
  • Li L, Lu K. Tests for the equality of two processes' spectral densities with unequal lengths using wavelet methods. J Time Ser Anal. 2018;39:4–27. doi: 10.1111/jtsa.12255
  • Decowski J, Li L. Wavelet-based tests for comparing two time series with unequal lengths. J Time Ser Anal. 2015;36:189–208. doi: 10.1111/jtsa.12101
  • Dette H, Paparoditis E. Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities. J Roy Statist Soc B. 2009;71:831–857. doi: 10.1111/j.1467-9868.2009.00709.x
  • Diggle PJ, Fisher NI. Nonparametric comparison of cumulative periodograms. Appl Statist. 1991;40:423–434. doi: 10.2307/2347522
  • Fokianos K, Savvides A. On comparing several spectral densitie. Technometrics. 2008;50:317–331. doi: 10.1198/004017008000000244
  • Jentsch C, Pauly M. Testing equality of spectral densities using randomization techniques. Bernoulli. 2015;21:697–739. doi: 10.3150/13-BEJ584
  • Jin L. A data-driven test to compare two or multiple time series. Comput Statist Data Anal. 2011;55:2183–2196. doi: 10.1016/j.csda.2011.01.013
  • Lu K, Li L. On fan's adaptive Neyman tests for comparing two spectral densities. J Statist Comput Simul. 2013;89:1585–1601. doi: 10.1080/00949655.2012.667101
  • Politis DN, Romano JP. A general resampling scheme for triangular arrays of α-mixing random variables with application to the problem of spectral density estimation. Ann Statist. 1992;20:1985–2007. doi: 10.1214/aos/1176348899
  • Lund R, Bassily H, Vidakovic B. Testing equality of stationary autocovariances. J Time Ser Anal. 2009;30:332–348. doi: 10.1111/j.1467-9892.2009.00616.x
  • Alonso A, Maharaj EA. Comparison of time series using subsampling. Comput Statist Data Anal. 2006;50:2589–2599. doi: 10.1016/j.csda.2005.04.010
  • Horowitz JL, Lobato I, Nankervis JC, et al. Bootstrapping the box-pierce q test: a Robust test of uncorrelatedness. J Econom. 2006;133:841–862. doi: 10.1016/j.jeconom.2005.06.014
  • Davison AC, Hinkley DV. Bootstrap methods and their application. Cambridge: Cambridge University Press; 1997.
  • Brockwell JP, Davis AR. Time series: theory and methods. 2nd ed. New York: Springer; 1991.
  • Su N, Lund R. Multivariate versions of Bartlett's formula. J Time Ser Anal. 2012;105:18–31.
  • Carlstein E. The use of subseries values for estimating the variance of a general statistic from a stationary sequence. Ann Statist. 1986;14:1171–1179. doi: 10.1214/aos/1176350057
  • Kunsch HR. The jackknife and the bootstrap for general stationary observations. Ann Statist. 1989;17:1217–1241. doi: 10.1214/aos/1176347265
  • Aerts M, Claeskens G, Hart JD. Testing lack of fit in multiple regression. Biometrika. 2000;87:405–424. doi: 10.1093/biomet/87.2.405
  • Grant AJ. Parametric methods for time series discrimination. PhD thesis, Macquarie University, Sydney, Australia; 2018.
  • Hannan EJ, Deistler M. The statistical theory of linear systems. New York: Wiley; 1998.
  • Politis DN, White H. Automatic block-length selection for the dependent bootstrap. Econom Rev. 2004;23:53–70. doi: 10.1081/ETC-120028836
  • Box G, Jenkins G. Time series analysis, forecasting and control. 4th ed. Hoboken (NJ): John Wiley & Sons; 2015.

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