Description Usage Arguments Details Value Methods (by generic) References See Also Examples
GIRF
estimates generalized impulse response function for
a structural GMVAR, StMVAR, or GStMVAR model.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  GIRF(
gsmvar,
which_shocks,
shock_size = 1,
N = 30,
R1 = 250,
R2 = 250,
init_regimes = 1:sum(gsmvar$model$M),
init_values = NULL,
which_cumulative = numeric(0),
scale = NULL,
scale_type = c("instant", "peak"),
ci = c(0.95, 0.8),
include_mixweights = TRUE,
ncores = 2,
plot_res = TRUE,
seeds = NULL,
...
)
## S3 method for class 'girf'
plot(x, add_grid = FALSE, margs, ...)
## S3 method for class 'girf'
print(x, ..., digits = 2, N_to_print)

gsmvar 
an object of class 
which_shocks 
a numeric vector of length at most d
( 
shock_size 
a nonzero scalar value specifying the common size for all scalar components of the structural shock. Note that the conditional covariance matrix of the structural shock is an identity matrix and that the (generalized) impulse responses may not be symmetric to the sign and size of the shock. 
N 
a positive integer specifying the horizon how far ahead should the generalized impulse responses be calculated. 
R1 
the number of repetitions used to estimate GIRF for each initial value. 
R2 
the number of initial values to be drawn from a stationary
distribution of the process or of a specific regime? The confidence bounds
will be sample quantiles of the GIRFs based on different initial values.
Ignored if the argument 
init_regimes 
a numeric vector of length at most M and elements
in 1,...,M specifying the regimes from which the initial values
should be generated from. The initial values will be generated from a
mixture distribution with the mixture components being the stationary
distributions of the specific regimes and the (proportional) mixing weights
given by the mixing weight parameters of those regimes. Note that if

init_values 
a size (pxd) matrix specifying the initial values, where d is the number
of time series in the system. The last row will be used as initial values for the first lag,
the second last row for second lag etc. If not specified, initial values will be drawn according to
mixture distribution specifed by the argument 
which_cumulative 
a numeric vector with values in 1,...,d
( 
scale 
should the GIRFs to some of the shocks be scaled so that they
correspond to a specific magnitude of instantaneous or peak response
of some specific variable (see the argument 
scale_type 
If argument 
ci 
a numeric vector with elements in (0, 1) specifying the confidence levels of the confidence intervals. 
include_mixweights 
should the generalized impulse response be
calculated for the mixing weights as well? 
ncores 
the number CPU cores to be used in parallel computing. Only single core computing is supported if an initial value is specified (and the GIRF won't thus be estimated multiple times). 
plot_res 

seeds 
a length 
... 
arguments passed to 
x 
object of class 
add_grid 
should grid be added to the plots? 
margs 
numeric vector of length four that adjusts the

digits 
the number of decimals to print 
N_to_print 
an integer specifying the horizon how far to print the estimates and confidence intervals. The default is that all the values are printed. 
The model needs to be structural in order for this function to be
applicable. A structural GMVAR, StMVAR, or GStMVAR model can be estimated
by specifying the argument structural_pars
in the function fitGSMVAR
.
The confidence bounds reflect uncertainty about the initial state (but currently not about the parameter estimates) if initial values are not specified. If initial values are specified, there won't currently be confidence intervals. See the cited paper by Virolainen (2020) for details about the algorithm.
Note that if the argument scale
is used, the scaled responses of
the mixing weights might be more than one in absolute valie.
Returns a class 'girf'
list with the GIRFs in the first
element ($girf_res
) and the used arguments the rest. The first
element containing the GIRFs is a list with the mth element
containing the point estimates for the GIRF in $point_est
(the first
element) and confidence intervals in $conf_ints
(the second
element). The first row is for the GIRF at impact (n=0), the second
for n=1, the third for n=2, and so on.
plot
: plot method
print
: print method
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485498.
Virolainen S. 2020. Structural Gaussian mixture vector autoregressive model. Unpublished working paper, available as arXiv:2007.04713.
Virolainen S. 2021. Gaussian and Student's t mixture vector autoregressive model. Unpublished working paper, available as arXiv:2109.13648.
@keywords internal
GFEVD
, fitGSMVAR
, GSMVAR
,
gsmvar_to_sgsmvar
, reorder_W_columns
,
swap_W_signs
, simulate.gsmvar
,
predict.gsmvar
, profile_logliks
,
quantile_residual_tests
, LR_test
,
Wald_test
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 
# These are longrunning examples that use parallel computing.
# It takes approximately 30 seconds to run all the below examples.
# Structural GMVAR(2, 2), d=2 model identified with signconstraints:
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(gdpdef, p=2, M=2, params=params22s,
structural_pars=list(W=W_22))
mod22s
# Alternatively, use:
#fit22s < fitGSMVAR(gdpdef, p=2, M=2, structural_pars=list(W=W_22),
# ncalls=20, seeds=1:20)
# To obtain an estimated version of the same model.
# Estimating the GIRFs of both structural shocks with initial values
# drawn from the stationary distribution of the process,
# 12 periods ahead, confidence levels 0.95 and 0.8:
girf1 < GIRF(mod22s, N=12, R1=100, R2=100)
girf1
plot(girf1)
# Estimating the GIRF of the second shock only, 12 periods ahead
# and shock size 1, initial values drawn from the stationary distribution
# of the first regime, confidence level 0.9:
girf2 < GIRF(mod22s, which_shocks=2, shock_size=1, N=12, init_regimes=1,
ci=0.9, R1=100, R2=100)
# Estimating the GIRFs of both structural shocks, negative one standard
# error shock, N=20 periods ahead, estimation based on 200 Monte Carlo
# simulations, and fixed initial values given by the last p observations
# of the data:
girf3 < GIRF(mod22s, shock_size=1, N=20, R1=200,
init_values=mod22s$data)

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