gmvarkit (version 2.1.4)

stmvar_to_gstmvar: Estimate a G-StMVAR model based on a StMVAR model that has large degrees of freedom parameters

Description

stmvar_to_gstmvar estimates a G-StMVAR model based on a StMVAR model that has large degrees of freedom parameters.

Usage

stmvar_to_gstmvar(
  gsmvar,
  estimate,
  calc_std_errors = estimate,
  maxdf = 100,
  maxit = 100
)

Value

Returns an object of class 'gsmvar' defining a G-StMVAR model based on the provided StMVAR (or G-StMVAR) model with the regimes that had large degrees of freedom parameters changed to GMVAR type.

Arguments

gsmvar

an object of class 'gsmvar', typically created with fitGSMVAR or GSMVAR.

estimate

set TRUE if the new model should be estimated with a variable metric algorithm using the StMAR model parameter value as the initial value. By default TRUE iff the model contains data.

calc_std_errors

set TRUE if the approximate standard errors should be calculated.

maxdf

regimes with degrees of freedom parameter value larger than this will be turned into GMVAR type.

maxit

the maximum number of iterations for the variable metric algorithm. Ignored if estimate==FALSE.

Details

If a StMVAR model contains large estimates for the degrees of freedom parameters, one should consider switching to the corresponding G-StMAR model that lets the corresponding regimes to be GMVAR type. stmvar_to_gstmvar does this switch conveniently. Also G-StMVAR models are supported if some of the StMVAR type regimes have large degrees of freedom paraters.

Note that if the model imposes constraints on the autoregressive parameters, or if a structural model imposes constraints on the lambda parameters, and the ordering the regimes changes, the constraints are removed from the model. This is because of the form of the constraints that does not generally allow to switch the ordering of the regimes. If you wish to keep the constraints, you may construct the resulting G-StMVAR model parameter vector by hand, redefine your constraints accordingly, build the model with the function GSMVAR, and then estimate it with the function iterate_more. Alternatively, you can always directly estimate the constrained G-StMVAR model with the function fitGSMVAR.

References

• Muirhead R.J. 1982. Aspects of Multivariate Statistical Theory, Wiley.

• Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.

• 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.

• Virolainen S. 2025. A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics, 43, 1, 44-54.

See Also

fitGSMVAR, GSMVAR, GIRF, reorder_W_columns, swap_W_signs, gsmvar_to_sgsmvar

Examples

# \donttest{
# StMVAR(1, 2), d=2 model:
params12t <- c(0.5453, 0.1157, 0.331, 0.0537, -0.0422, 0.7089, 0.4181, 0.0018,
  0.0413, 1.6004, 0.4843, 0.1256, -0.0311, -0.6139, 0.7221, 1.2123, -0.0357,
  0.1381, 0.8337, 7.5564, 90000)
mod12t <- GSMVAR(gdpdef, p=1, M=2, params=params12t, model="StMVAR")
mod12t

# Switch to the G-StMVAR model:
mod12gs <- stmvar_to_gstmvar(mod12t)
mod12gs
# }