View source: R/generateParams.R
random_ind2 | R Documentation |
random_ind2
generates random mean-parametrized parameter vector
that is always stationary.
random_ind2(
p,
M,
d,
model = c("GMVAR", "StMVAR", "G-StMVAR"),
same_means = NULL,
weight_constraints = NULL,
structural_pars = NULL,
mu_scale,
mu_scale2,
omega_scale,
ar_scale = 1,
W_scale,
lambda_scale
)
p |
a positive integer specifying the autoregressive order of the model. |
M |
|
d |
the number of time series in the system. |
model |
is "GMVAR", "StMVAR", or "G-StMVAR" model considered? In the G-StMVAR model, the first |
same_means |
Restrict the mean parameters of some regimes to be the same? Provide a list of numeric vectors
such that each numeric vector contains the regimes that should share the common mean parameters. For instance, if
|
weight_constraints |
a numeric vector of length |
structural_pars |
If
See Virolainen (2022) for the conditions required to identify the shocks and for the B-matrix as well (it is |
mu_scale |
a size |
mu_scale2 |
a size |
omega_scale |
a size |
ar_scale |
a positive real number adjusting how large AR parameter values are typically proposed in construction
of the initial population: larger value implies larger coefficients (in absolute value). After construction of the
initial population, a new scale is drawn from |
W_scale |
a size |
lambda_scale |
a length If the lambda parameters are constrained with the This argument is ignored if As with omega_scale and W_scale, this argument should be adjusted carefully if specified by hand. NOTE that if lambdas are constrained in some other way than restricting some of them to be identical, this parameter should be adjusted accordingly in order to the estimation succeed! |
The coefficient matrices are generated using the algorithm proposed by Ansley
and Kohn (1986) which forces stationarity. It's not clear in detail how ar_scale
exactly affects the coefficient matrices but larger ar_scale
seems to result in larger
AR coefficients. Read the cited article by Ansley and Kohn (1986) and the source code
for more information.
The covariance matrices are generated from (scaled) Wishart distribution.
Models with AR parameters constrained are not supported!
Returns random mean-parametrized parameter vector that has the same form as the argument params
in the other functions, for instance, in the function loglikelihood
.
Ansley C.F., Kohn R. 1986. A note on reparameterizing a vector autoregressive moving average model to enforce stationarity. Journal of statistical computation and simulation, 24:2, 99-106.
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Virolainen S. 2022. Structural Gaussian mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks. Unpublished working paper, available as arXiv:2007.04713.
Virolainen S. 2022. Gaussian and Student's t mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area. Unpublished working paper, available as arXiv:2109.13648.
@keywords internal
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