autocorrelation_coeff_plot: Plot Confidence Bounds of Estimated Functional...

In dpetoukhov/wwntests: Hypothesis Tests for Functional Time Series

Description

autocorrelation_coeff_plot Computes the 1-alpha upper confidence bounds for the functional autocorrelation coefficients at lags h = 1:K under both weak white noise (WWN) and strong white noise (SWN) assumptions. It plots the coefficients as well as the bounds for all lags h = 1:K. Note, the SWN bound is constant, while the WWN is dependent on the lag.

Usage

autocorrelation_coeff_plot(f_data, K = 20, alpha = 0.05, M = NULL,
  low_disc = FALSE)

Arguments

f_data	The functional data matrix with observed functions in the columns.
K	A positive Integer value. The maximum lag for which to compute the single-lag test (tests will be computed for lags h in 1:K).
alpha	A numeric value between 0 and 1 specifying the significance level to be used in the single-lag test. The default value is 0.05.
M	A positive Integer value. Determines the number of Monte-Carlo simulations employed in the Welch-Satterthwaite approximation of the limiting distribution of the test statistics, for each test.
low_disc	A Boolean value, FALSE by default. If given TRUE, uses low-discrepancy sampling in the Monte-Carlo method. Note, low-discrepancy sampling will yield deterministic results. Requires the 'fOptions' package.

Details

This function computes and plots autocorrelation coefficients at lag h, for h in 1:K. It also computes an estimated asymptotic 1 - alpha confidence bound, under the assumption that the series forms a weak white noise. Additionally, it computes a similar (constant) bound under the assumption the series form a strong white noise. Please see the vignette or the references for a more complete treatment.

Value

Plot of the estimated autocorrelation coefficients for lags h in 1:K with the weak white noise 1-alpha upper confidence bound for each lag, as well as the constant strong white noise 1-alpha confidence bound.

References

[1] Kokoszka P., & Rice G., & Shang H.L. (2017). Inference for the autocovariance of a functional time series under conditional heteroscedasticity. Journal of Multivariate Analysis, 162, 32-50.

Examples

b <- brown_motion(75, 40)
autocorrelation_coeff_plot(b)
autocorrelation_coeff_plot(b, M = 200, low_disc = TRUE)

dpetoukhov/wwntests documentation built on Jan. 3, 2020, 12:14 a.m.