est_cfarh: Estimation of a CFAR Process with Heteroscedasticity and...

In NTS: Nonlinear Time Series Analysis

Estimation of a CFAR Process with Heteroscedasticity and Irregualar Observation Locations

Description

Estimation of a CFAR process with heteroscedasticity and irregualar observation locations.

Usage

est_cfarh(
  f,
  weight,
  p = 2,
  grid = 1000,
  df_b = 5,
  num_obs = NULL,
  x_pos = NULL
)

Arguments

f	the functional time series.
weight	the covariance functions of noise process.
p	the CFAR order.
grid	the number of gird points used to construct 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 returns a list with components:

phi_coef	the estimated spline coefficients for convolutional function(s).
phi_func	the estimated convolutional function(s).
rho	estimated rho for O-U process (noise process).
sigma	estimated sigma for O-U process (noise process).

References

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

NTS documentation built on Sept. 25, 2023, 1:08 a.m.