simulate_from_regime {sstvars} | R Documentation |
Simulate observations from a regime of a STVAR model
Description
simulate_from_regime
allows to simulate observations from a single
regime of a STVAR model
Usage
simulate_from_regime(
stvar,
regime = 1,
nsim = 1,
init_values = NULL,
use_transweights = TRUE
)
Arguments
stvar |
an object of class |
regime |
an integer in |
nsim |
number of observations to be simulated. |
init_values |
a size |
use_transweights |
if |
Details
Does not take random number generator seed as an argument to avoid unwanted behavior,
because simulate_from_regime
is mostly called from simulate.stvar
that takes a seed as its argument, and simulate_from_regime
calls simulate.stvar
to simulate the observations.
Specifically, simulate_from_regime
generates a STVAR model from the given regime, sets up the initial values to the
(if not specified), and then calls simulate.stvar
accordingly.
Value
- If
use_transweights=FALSE
: Returns a
(nsim \times d)
matrix such that thet
th row contains thet
th simulated observation.- If
use_transweights=TRUE
: Returns a
(p \times d)
such that thet
th row constrains thet
th observations.
References
Anderson H., Vahid F. 1998. Testing multiple equation systems for common nonlinear components. Journal of Econometrics, 84:1, 1-36.
Hansen B.E. 1994. Autoregressive Conditional Density estimation. Journal of Econometrics, 35:3, 705-730.
Kheifets I.L., Saikkonen P.J. 2020. Stationarity and ergodicity of Vector STAR models. International Economic Review, 35:3, 407-414.
Lanne M., Virolainen S. 2024. A Gaussian smooth transition vector autoregressive model: An application to the macroeconomic effects of severe weather shocks. Unpublished working paper, available as arXiv:2403.14216.
Lütkepohl H. 2005. New Introduction to Multiple Time Series Analysis, Springer.
McElroy T. 2017. Computation of vector ARMA autocovariances. Statistics and Probability Letters, 124, 92-96.
Kilian L., Lütkepohl H. 20017. Structural Vector Autoregressive Analysis. 1st edition. Cambridge University Press, Cambridge.
Tsay R. 1998. Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93:443, 1188-1202.
Virolainen S. 2024. Identification by non-Gaussianity in structural threshold and smooth transition vector autoregressive models. Unpublished working paper, available as arXiv:2404.19707.