F_test_cfarh: F Test for a CFAR Process with Heteroscedasticity and...

In ConvFuncTimeSeries/test_t: Nonlinear time series analysis

Description

F test for a CFAR process with heteroscedasticity and irregular observation locations to specify the CFAR order.

Usage

F_test_cfarh(f, weight, p.max = 3, grid = 1000, df_b = 10,
  num_obs = NULL, x_pos = NULL)

Arguments

f	the functional time series.
weight	the covariance functions for noise process.
p.max	maximum CFAR order. Default is 3.
grid	the number of gird points used to constrct the functional time series and noise process. Default is 1000.
df_b	the degrees of freedom for natural cubic splines. Default is 10.
num_obs	the numbers of observations. It is a t-by-1 vector, where t is the length of time.
x_pos	the observation location matrix. If the locations are regular, it is a t-by-(n+1) matrix with all entries 1/n.

Value

The function outputs F test statistics and their p-values.

References

Liu, X., Xiao, H., and Chen, R. (2016) Convolutional autoregressive models for functional time series. Journal of Econometrics, 194, 263-282.

Examples

phi_func1= function(x){
return(0.5*x^2+0.5*x+0.13)
}
phi_func2= function(x){
return(0.7*x^4-0.1*x^3-0.15*x)
}
grid=1000
testh=g_cfar2h(100,grid,1,40,5,phi_func1=phi_func1,phi_func2=phi_func2)
#obtain discret observations
f_grid=testh$cfar2h
num_obs=testh$num_obs
x_pos=testh$x_pos
t=dim(f_grid)[1]
n=max(num_obs)
index=matrix(0,t,n)
x_grid=seq(0,1,by=1/grid)
f=matrix(0,t,n)
for(i in 1:t){
for(j in 1:num_obs[i]){
	index[i,j]=which(abs(x_pos[i,j]-x_grid)==min(abs(x_pos[i,j]-x_grid)))
}
f[i,1:num_obs[i]]=f_grid[i,index[i,1:num_obs[i]]];
}
weight0= function(w){
return((w<=0.6)*exp(-10*w)+(w>0.6)*(exp(-6)+0.2*(w-0.6))+0.1);
}
const=sum(weight0(x_grid)/(grid+1))
weight= function(w){
return(((w<=0.6)*exp(-10*w)+(w>0.6)*(exp(-6)+0.2*(w-0.6))+0.1)/const)
}
F_test_cfarh(f,weight,3,1000,5,num_obs,x_pos)

ConvFuncTimeSeries/test_t documentation built on May 29, 2019, 1:39 p.m.