Description Usage Arguments Value References Examples
Generate a convolutional functional autoregressive process of order 2 with heteroscedasticity, irregular observation locations.
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tmax |
length of time. |
grid |
the number of grid points used to construct the functional time series. |
rho |
parameter for O-U process (noise process). |
min_obs |
the minimum number of observations at each time. |
pois |
the mean for Poisson distribution. The number of observations at each follows a Poisson distribution plus min_obs. |
phi_func1 |
the first convolutional function. Default is 0.5*x^2+0.5*x+0.13. |
phi_func2 |
the second convolutional function. Default is 0.7*x^4-0.1*x^3-0.15*x. |
weight |
the weight function to determine the standard deviation of O-U process (noise process). Default is 1. |
ini |
the burn-in period. |
The function returns a list with components:
cfar2 |
a tmax-by-(grid+1) matrix following a CFAR(1) process. |
epsilon |
the innovation at time tmax. |
Liu, X., Xiao, H., and Chen, R. (2016) Convolutional autoregressive models for functional time series. Journal of Econometrics, 194, 263-282.
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