View source: R/generateParams.R
random_ind | R Documentation |
random_ind
generates random mean-parametrized parameter vector that may not be stationary.
random_ind(
p,
M,
d,
model = c("GMVAR", "StMVAR", "G-StMVAR"),
constraints = NULL,
same_means = NULL,
weight_constraints = NULL,
structural_pars = NULL,
mu_scale,
mu_scale2,
omega_scale,
W_scale,
lambda_scale,
ar_scale2 = 1
)
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 |
constraints |
a size |
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 (forthcoming) 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 |
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! |
ar_scale2 |
a positive real number adjusting how large AR parameter values are typically proposed in some random mutations (if AR constraints are employed, in all random mutations): larger value implies smaller coefficients (in absolute value). Values larger than 1 can be used if the AR coefficients are expected to be very small. If set smaller than 1, be careful as it might lead to failure in the creation of stationary parameter candidates |
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
.
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Virolainen S. (forthcoming). A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics.
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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