Description Usage Arguments Details Value References See Also
This function simulates responses for one-dimensional factor models.
1 2 | simulateDSFM1D(model = "ns", n = 100, x1 = 1:30, L = 3, var = 5e-04,
beta = c(0.065, -0.015, 0.05), tau = c(0.5, 6))
|
model |
the type of model to be generated. To choose between |
n |
the number of observations. |
x1 |
a vector of covariates. |
L |
the number of factors for the DSFM. |
var |
the error \varepsilon_{t,j} of the models. Allows to control the noise. |
beta |
a vector of dimension (1x3) controlling the starting values of the VAR(1) process. |
tau |
a vector of dimension (1x2) specifying the parameters Τ_1 and Τ_2 of the Extended Nelson-Siegel model. These parameters are constant. |
Two different way of generating data are available: using an Extended
Nelson-Siegel model following Bliss (1997) "ns"
with a predefined
VAR(1) process, or a Dynamic Semiparametric Factor Model "dsfm"
with predefined factors functions.
This function is used for example purpose, only few parameters are available
to control the simulation.
The starting values to simulate the Extended Nelson-Siegel model are taken following Linton and al. (2001).
The factors loadings of the DSFM are simulated from three independant AR(1) process and the factors functions are predefined to be orthogonals.
simulateDSFM1D
returns a list containing:
|
an object of class |
|
the simulated data in a more usual format. |
|
the simulated factor loadings. |
|
the vector of the covariates. |
Depending on the model simulated, the functions returns also:
|
the factors functions used to compute the DSFM. |
|
the constant Τ_1 and Τ_2 used to compute the Extended Nelson-Siegel model. |
Linton, Oliver et al. (2001). "Yield Curve Estimation by Kernel Smoothing Methods". In: Journal of Econometrics 105.1, pp. 185-223.
Bliss, Robert R.(1997). "Testing Term Structure Estimation Methods". In: Advances in Futures and Options Research 9, pp. 197-231.
DSFM1DData
, DSFM
, DSFM1D
.
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