VMB: The vectorized moving block bootstrap
In autocorr: Testing Autocorrelation through bootstrap and surrogate methods

Description Usage Arguments Details Value Author(s) References See Also

This function VMB performs the vectorized moving block bootstrap to carry out tests on autocorrelations.

1	VMB(acf.est, ahat, data, l, a1, a2, boot, lagmax)

`acf.est`	a vector of sample autocorrelation estimates up to lagmax.
`ahat`	a vector of estimated acceleration constants for autocorrelations up to lagmax. An object returned by JN.VMB
`data`	a vector of time series data.
`l`	block size for the vectorized moving block bootstrap.
`a1`	a scaler or a numeric vector of percentage(s) for the lower limit(s) of confidence intervals.
`a2`	a scaler or a numeric vector of percentage(s) for the lower limit(s) of confidence intervals.
`boot`	number of bootstrap replications.
`lagmax`	maximum lag at which to calculate autocorrelations.

The vectorized moving block bootstrap constructs blocks by splitting the original time series data of length n.t into n.t-l+1 blocks of length l. These n.t-l+1 blocks are then resampled with replacement. Observations in the selected blocks and observations h lags later are paired and are used to compute the estimate for the autocorrelation at lag h.

In the presence of missing values, estimates of autocorrelations are computed from complete cases, which may not be valid.

a list containing two components: se: standard error estimates for autocorrelations. CI: a list of estimated confidence intervals for autocorrelations. Contain two components: per: percentile intervals BCa: bias-corrected and accelerated intervals.

Zijun Ke <keziyun@mail.sysu.edu.cn> and Zhiyong Zhang <zhiyongzhang@nd.edu>

Kunsch, H. (1989). The jackknife and the bootstrap for general stationary observations. The Annals of Statistics, 17(3), 1217-1241. Zhang, G., & Browne, M. W. (2010). Bootstrap standard error estimates in dynamic factor analysis. Multivariate Behavioral Research, 45(453-482).

JN.VMB,pair.VMB,corr.VMB

autocorr documentation built on May 2, 2019, 6:12 p.m.