fkpsst {STFTS}

Functional KPSS Test

Description

KPSS test for functional time series with different methods on determining the critical values of the test statistic. The Monte Carlo method (for a large sample size) was constructed in Kokoszka and Young (2016) and the bootstrap-based methods (both the simple bootstrap method and moving block bootstrap method, for a small/moderate sample size) were constructed in Chen and Pun (2019).

Usage

fkpsst(
  X,
  ALPHA = 0.05,
  METHOD = "MC",
  K = default_kernel,
  h_power = 2/5,
  est_ev = nrow(X),
  MCNsim = 10000,
  sbb_set = NULL,
  M = 1000,
  b = ceiling((2 * nrow(X))^(1/3))
)

Arguments

X

The functional time series being tested, inputted in a matrix form with each row representing each observation of the functional data values on equidistant points of any prespecified interval.

ALPHA

Significance level of the test. The default value is 5%.

METHOD

Method to determine the critical value of the test statistic. The default value 'METHOD="MC"' represents the Monte Carlo method. 'METHOD="SB"' represents the simple bootstrap method and 'METHOD="MBB"' represents the moving block bootstrap method.

K

Kernel function in the estimation of the long-run covariance function, which is only effective in the Monte Carlo method. The default function is 'default_kernel' function in this package.

h_power

Power of sample size 'N' (valued in (0,1)) for the smoothing bandwidth, which is only effective in the Monte Carlo method. The default value is 2/5.

est_ev

Number of the largest eigenvalues chosen to estimate the limiting distribution, which is only effective in the Monte Carlo method. The default value is the sample size 'N'.

MCNsim

Number of Monte Carlo datasets generated in the Monte Carlo method, which is only effective in Monte Carlo method. The default value is 10000.

sbb_set

A vector of independent simulated data generated from the function 'dataset_sbb', which is only effective and essential in Monte Carlo method.

M

Number of bootstrap datasets generated in the bootstrap method, which is only effective in bootstrap methods. The default value is 1000.

b

Block length used in the moving block bootstrap method, which is only effective in the moving block bootstrap method. The default value is ceiling((2N)^(1/3)), where 'N' is the sample size.

Value

The result of the test is presented with the value of test statistic and its p-value under the null hypothesis of trend stationarity.

References

Chen, Y., & Pun, C. S. (2019). A bootstrap-based KPSS test for functional time series. Journal of Multivariate Analysis, 174, 104535.

Kokoszka, P., & Young, G. (2016). KPSS test for functional time series. Statistics, 50(5), 957-973.

Examples

N<-100
EX<-matrix(rep(0,N*100),ncol=100)
EXI<-rep(0,100)
set.seed(1)
EXI[1]<-rnorm(1,0,1)
for (j in 2:100) {EXI[j]<-EXI[j-1]+rnorm(1,0,1)}
for (i in 1:N) {
temp<-rnorm(100,0,1)
EX[i,1]<-temp[1]
for (j in 2:100) {
EX[i,j]<-EX[i,j-1]+temp[j]
}
}
fkpsst(X=EX,METHOD="SB")