View source: R/parameterReforms.R
sort_W_and_lambdas | R Documentation |
sort_W_and_lambdas
sorts the columns of W matrix by sorting the lambda parameters of
the second regime to increasing order.
sort_W_and_lambdas(p, M, d, params, model = c("GMVAR", "StMVAR", "G-StMVAR"))
p |
a positive integer specifying the autoregressive order of the model. |
M |
|
d |
the number of time series in the system. |
params |
a real valued vector specifying the parameter values.
Above, In the GMVAR model, The notation is similar to the cited literature. |
Only structural models are supported (but there is no need to provide structural_pars). This function does not sort the constraints of the W matrix but just sorts the columns of the W matrix and the lambda parameters. It is mainly used in the genetic algorithm to assist estimation with better identification when the constraints are not itself strong for identification of the parameters (but are invariant to different orderings of the columns of the W matrix).
Before using this function, make sure the parameter vector is sortable: the constraints on the W matrix is invariant to different orderings of the columns, there are no zero restrictions, and there are no constraints on the lambda parameters.
Returns the sorted parameter vector (that implies the same reduced form model).
No argument checks!
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 Unpublished working paper, available as arXiv:2109.13648.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.