gmvarkit (version 2.1.4)

swap_W_signs: Swap all signs in pointed columns a the \(W\) matrix of a structural GMVAR, StMVAR, or G-StMVAR model.

Description

swap_W_signs swaps all signs in pointed columns a the \(W\) matrix of a structural GMVAR, StMVAR, or G-StMVAR model. Consequently, signs in the columns of the B-matrix are also swapped accordingly.

Usage

swap_W_signs(gsmvar, which_to_swap)

Value

Returns an object of class 'gsmvar' defining a structural GMVAR, StMVAR, or G-StMVAR model with the modified structural parameters and constraints.

Arguments

gsmvar

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

which_to_swap

a numeric vector of length at most \(d\) and elemnts in \(1,..,d\) specifying the columns of \(W\) whose sign should be swapped.

Details

All signs in any column of \(W\) can be swapped without changing the implied reduced form model. Consequently, also the signs in the columns of the B-matrix are swapped. Note that the sign constraints imposed on \(W\) (or the B-matrix) are also swapped in the corresponding columns accordingly.

Also the order of the columns of \(W\) can be changed (without changing the implied reduced form model) as long as the order of lambda parameters is also changed accordingly. This can be done with the function reorder_W_columns.

References

• 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 Unpublished working paper, available as arXiv:2109.13648.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

See Also

fitGSMVAR, GSMVAR, GIRF, reorder_W_columns, gsmvar_to_sgsmvar, stmvar_to_gstmvar

Examples

# Structural GMVAR(2, 2), d=2 model identified with sign-constraints:
params22s <- c(0.36, 0.121, 0.484, 0.072, 0.223, 0.059, -0.151, 0.395,
  0.406, -0.005, 0.083, 0.299, 0.218, 0.02, -0.119, 0.722, 0.093, 0.032,
  0.044, 0.191, 0.057, 0.172, -0.46, 0.016, 3.518, 5.154, 0.58)
W_22 <- matrix(c(1, 1, -1, 1), nrow=2, byrow=FALSE)
mod22s <- GSMVAR(p=2, M=2, d=2, params=params22s, structural_pars=list(W=W_22))
mod22s

# The same reduced form model, with signs in the second column of W swapped:
swap_W_signs(mod22s, which_to_swap=2)

# The same reduced form model, with signs in both column of W swapped:
swap_W_signs(mod22s, which_to_swap=1:2)

#' # Structural G-StMVAR(2, 1, 1), d=2 model identified with sign-constraints:
mod22gss <- GSMVAR(p=2, M=c(1, 1), d=2, params=c(params22s, 10), model="G-StMVAR",
                   structural_pars=list(W=W_22))
mod22gss

# The same reduced form model, with signs in the first column of W swapped:
swap_W_signs(mod22gss, which_to_swap=1)