gof_far | R Documentation |
It fits a FAR(1) model and then assesses the cumulative significance of lagged
autocovariance operators from the model residuals, up to a user-selected maximum lag H
.
gof_far(
f_data,
H = 10,
M = NULL,
alpha = 0.05,
pplot = FALSE,
residual = FALSE,
suppress_raw_output = FALSE,
suppress_print_output = FALSE
)
f_data |
A |
H |
A positive integer specifying the maximum lag for which test statistics are computed. |
M |
A positive integer specifying the number of Monte Carlo simulations used to approximate the null distribution.
If |
alpha |
A numeric value between 0 and 1 specifying the significance level. |
pplot |
A Boolean value. If TRUE, the function will produce a plot of p-values of the test
as a function of maximum lag |
residual |
A data frame. If TRUE, the function will provide the residuals obtained from fitting the FAR(1) model. |
suppress_raw_output |
A Boolean value, FALSE by default. If TRUE, the function will not return the list containing the p-value, quantile, and statistic. |
suppress_print_output |
A Boolean value, FALSE by default. If TRUE, the function will not print any output to the console. |
If suppress_raw_output = FALSE, a list containing the test statistic, the (1-\alpha)
quantile of the
limiting distribution, and the p-value computed from the specified hypothesis test. Also prints output
containing a short description of the test, the p-value, and additional information about the test if
suppress_print_output = FALSE.
[1] Kim, M., Kokoszka, P., & Rice, G. (2023). White noise testing for functional time series. Statistic Surveys, 17, 119-168.
yd_far <- dgp.far(J=50, N=100, S=0.7, p=2, kernel = "Gaussian", burn_in = 50)
gof_far(yd_far, H=5, pplot=TRUE)
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