Estimation of a CFAR process with heteroscedasticity and irregualar observation locations.
est_cfarh(
f,
weight,
p = 2,
grid = 1000,
df_b = 5,
num_obs = NULL,
x_pos = NULL
)The function returns a list with components:
the estimated spline coefficients for convolutional function(s).
the estimated convolutional function(s).
estimated rho for O-U process (noise process).
estimated sigma for O-U process (noise process).
the functional time series.
the covariance functions of noise process.
the CFAR order.
the number of gird points used to construct the functional time series and noise process. Default is 1000.
the degrees of freedom for natural cubic splines. Default is 10.
the numbers of observations. It is a t-by-1 vector, where t is the length of time.
the observation location matrix. If the locations are regular, it is a t-by-(n+1) matrix with all entries 1/n.
Liu, X., Xiao, H., and Chen, R. (2016) Convolutional autoregressive models for functional time series. Journal of Econometrics, 194, 263-282.