Description Usage Arguments Details Value Author(s) See Also Examples
Generates a random instance of a partial cointegration model
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n |
Number of observations to generate |
beta |
A vector of factor loadings |
sigma_C |
A vector of standard deviations |
rho |
The coefficient of mean reversion |
sigma_R |
The standard deviation of the innovations of the random walk portion of the residual series |
sigma_M |
The standard deviation of the innovations of the mean-reverting portion of the residual series |
include.state |
If TRUE, then the output data.frame contains the innovations to the factors and residual series, as well as the state of the residual series. Default: FALSE |
robust |
If TRUE, then a t-distribution is used to generate the innovations. Otherwise, the innovations are normally distributed. Default: FALSE. |
nu |
The degrees of freedom parameter used for t-distributed innovations. Default: 5. |
Generates a random set of partially cointegrated vectors. On input, n
is the
length of the sequence to be generated. beta
is a vector of length k
representing the coefficients of the factor loadings, and sigma_C
is a
vector of length k representing the standard deviations of the increments
of the factor loadings.
Generates a random realization of the sequence
Y_t = β_1 F_{1,t} + β_2 F_{2,t} + ... + β_k F_{k,t} + M_t + R_t
F_{i,j} = F_{i,j-1} + δ_{i,j}
M_t = ρ m_{t-1} + ε_{M,t}
R_t = r_{t-1} + ε_{R,t}
δ_{i,j} ~ N(0, σ_{C,i}^2)
ε_{M,t} ~ N(0, σ_M^2)
ε_{R,t} ~ N(0, σ_R^2)
A data.frame
of n
rows representing the realization of the partially
cointegrated sequence.
If include.state
is FALSE
, returns an n x (k+1)
matrix whose columns
are y, F_1, F_2, ..., F_k
. If include.state is TRUE
, returns an
n x (2k + 6)
matrix whose columns are
y, F_1, F_2, ..., F_k, x, M, R, delta_1, delta_2, ..., delta_k, epsilon_M, epsilon_R
.
Matthew Clegg matthewcleggphd@gmail.com
Christopher Krauss christopher.krauss@fau.de
Jonas Rende jonas.rende@fau.de
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