simulate_stvar_int: INTERNAL Simulate method for class 'stvar' objects

Description

simulate_stvar_int is an internal simulate function for class 'stvar' objects.

Usage

simulate_stvar_int(
  object,
  nsim = 1,
  seed = NULL,
  ...,
  init_values = NULL,
  init_regime,
  ntimes = 1,
  use_stat_for_Gaus = FALSE,
  burn_in = 1000,
  exo_weights = NULL,
  drop = TRUE,
  girf_pars = NULL
)

Value

Returns a list containing the simulation results. If drop==TRUE and ntimes==1 (default), contains the following entries:

sample: a size (nsim$\times d$) matrix containing the simulated time series.
transition weights:: a size (nsim$\times M$) matrix containing the transition weights corresponding to the simulated sample.

Otherwise, returns a list with the following entries:

$sample: a size (nsim$\times d\times$ntimes) array containing the samples: the dimension [t, , ] is the time index, the dimension [, d, ] indicates the marginal time series, and the dimension [, , i] indicates the i:th set of simulations.
$transition_weights: a size (nsim$\times M \times$ntimes) array containing the transition weights corresponding to the sample: the dimension [t, , ] is the time index, the dimension [, m, ] indicates the regime, and the dimension [, , i] indicates the i:th set of simulations.

Arguments

object

an object of class 'stvar'.

nsim

number of observations to be simulated.

seed

set seed for the random number generator?

...

currently not in use.

init_values

a size $(p\times d)$ 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 from the regime specified in init_regimes (for Gaussian models only).

init_regime

an integer in $1,...,M$ specifying the regime from which the initial values should be generated from (using a simulation procedure with a burn-in period). For models with Gaussian conditional distribution, it is also possible to generate the starting values from the stationary distribution of a regime. See the details section.

ntimes

how many sets of simulations should be performed?

use_stat_for_Gaus

if TRUE and cond_dist=="Gaussian", uses the stationary distribution of a regime to generate the initial values for the simulation. Note that if stationary distribution is used, unlike with out simulation procedure, it is not guaranteed that the regime of interest has high transition weights at the given points of time. Note that if the model allows for unstable estimates (stvar$allow_unstab=TRUE), simulation procedure is always used.

burn_in

Burn-in period for simulating initial values from a regime when cond_dist!="Gaussian". See the details section.

exo_weights

if weight_function="exogenous", provide a size $(nsim \times M)$ matrix of exogenous transition weights for the regimes: [t, m] for a time $t$ and regime $m$ weight. Ignored if weight_function!="exogenous".

drop

if TRUE (default) then the components of the returned list are coerced to lower dimension if ntimes==1, i.e., $sample and $transition_weights will be matrices, and $component will be vector.

girf_pars

This argument is used internally in the estimation of generalized impulse response functions. Specifying something else than null to it will change how the function behaves. Should be a list with following elements

shock_numb: The number of the shock to be used in the GIRF.

shock_size

The size of the shock to be used in the GIRF.

cfact_pars

A list with the following elements (only for simulating counterfactuals):

cfact_metatype: The metatype of the counterfactual, c("counterfactual_fore", "counterfactual_girf").

cfact_type

The type of the counterfactual, c("fixed_path", "muted_response").

policy_var

The variable to be used as the policy variable.

mute_var

The variable to be used as the mute variable.

cfact_start

The start of the counterfactual period.

cfact_end

The end of the counterfactual period.

Details

When using init_regime to simulate the initial values from a given regime, we employ the following simulation procedure to obtain the initial values (unless use_stat_for_Gaus=TRUE and Gaussian model is considered). First, we set the initial values to the unconditional mean of the specified regime. Then, we simulate a large number observations from that regime as specified in the argument burn_in. Then, we simulate $p + 100$ observations more after the burn in period, and for the $100$ observations calculate the transition weights for them and take the consecutive $p$ observations that yield the highest transition weight for the given regime. For models with exogenous transition weights, takes just the last $p$ observations after the burn-in period.

The argument ntimes is intended for forecasting, which is used by the predict method (see ?predict.stvar).

References