GIRF: Estimate generalized impulse response function for a...

Description Usage Arguments Details Value Methods (by generic) References See Also Examples

View source: R/GIRFandGFEVD.R

Description

GIRF estimates generalized impulse response function for a structural GMVAR, StMVAR, or G-StMVAR model.

Usage

 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)

Arguments

gsmvar

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

which_shocks

a numeric vector of length at most d (=ncol(data)) and elements in 1,...,d specifying the structural shocks for which the GIRF should be estimated.

shock_size

a non-zero 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_value is specified.

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_regimes=1:M, the initial values are generated from the stationary distribution of the process and if init_regimes=m, the initial value are generated from the stationary distribution of the mth regime. Ignored if the argument init_values is specified.

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 init_regimes.

which_cumulative

a numeric vector with values in 1,...,d (d=ncol(data)) specifying which the variables for which the impulse responses should be cumulative. Default is none.

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)? Provide a length three vector where the shock of interest is given in the first element (an integer in 1,...,d), the variable of interest is given in the second element (an integer in 1,...,d), and the magnitude of its instantaneous or peak response (a non-zero real number) in the third element. If the GIRFs of multiple shocks should be scaled, provide a matrix which has one column for each of the shocks with the columns being the length three vectors described above.

scale_type

If argument scale is specified, should the GIRFs be scaled to match an instantaneous response "instant" or peak response "peak"? Ignored if scale is not specified.

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? TRUE or FALSE.

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

TRUE if the results should be plotted, FALSE if not.

seeds

a length R2 vector containing the random number generator seed for estimation of each GIRF. A single number of an initial value is specified. or NULL for not initializing the seed. Exists for creating reproducible results.

...

arguments passed to grid which plots grid to the figure.

x

object of class 'girf' generated by the function GIRF.

add_grid

should grid be added to the plots?

margs

numeric vector of length four that adjusts the [bottom_marginal, left_marginal, top_marginal, right_marginal] as the relative sizes of the marginals to the figures of the responses.

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.

Details

The model needs to be structural in order for this function to be applicable. A structural GMVAR, StMVAR, or G-StMVAR 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.

Value

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.

Methods (by generic)

References

@keywords internal

See Also

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

Examples

 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 long-running 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 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(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)
 

saviviro/gmvarkit documentation built on Oct. 25, 2021, 2:14 a.m.