Monte Carlo simulation of vector autoregression (VAR) model

uses additional
options specified by one or more name-value pair arguments. For example, you can specify simulation of multiple paths, exogenous
predictor data, or inclusion of future responses for conditional simulation.`Y`

= simulate(`Mdl`

,`numobs`

,`Name,Value`

)

`simulate`

performs conditional simulation using this process for all pages= 1,...,`k`

`numpaths`

and for each time= 1,...,`t`

`numobs`

.`simulate`

infers (or inverse filters) the innovations`E(`

from the known future responses,:,`t`

)`k`

`YF(`

. For,:,`t`

)`k`

`E(`

,,:,`t`

)`k`

`simulate`

mimics the pattern of`NaN`

values that appears in`YF(`

.,:,`t`

)`k`

For the missing elements of

`E(`

,,:,`t`

)`k`

`simulate`

performs these steps.Draw

`Z1`

, the random, standard Gaussian distribution disturbances conditional on the known elements of`E(`

.,:,`t`

)`k`

Scale

`Z1`

by the lower triangular Cholesky factor of the conditional covariance matrix. That is,`Z2`

=`L*Z1`

, where`L`

=`chol(C,'lower')`

and`C`

is the covariance of the conditional Gaussian distribution.Impute

`Z2`

in place of the corresponding missing values in`E(`

.,:,`t`

)`k`

For the missing values in

`YF(`

,,:,`t`

)`k`

`simulate`

filters the corresponding random innovations through the model`Mdl`

.

`simulate`

uses this process to determine the time origin*t*_{0}of models that include linear time trends.If you do not specify

`Y0`

, then*t*_{0}= 0.Otherwise,

`simulate`

sets*t*_{0}to`size(Y0,1)`

–`Mdl.P`

. Therefore, the times in the trend component are*t*=*t*_{0}+ 1,*t*_{0}+ 2,...,*t*_{0}+`numobs`

. This convention is consistent with the default behavior of model estimation in which`estimate`

removes the first`Mdl.P`

responses, reducing the effective sample size. Although`simulate`

explicitly uses the first`Mdl.P`

presample responses in`Y0`

to initialize the model, the total number of observations in`Y0`

(excluding any missing values) determines*t*_{0}.

[1]
Hamilton, J. D. *Time Series Analysis*. Princeton, NJ: Princeton University Press, 1994.

[2]
Johansen, S. *Likelihood-Based Inference in Cointegrated Vector Autoregressive Models*. Oxford: Oxford University Press, 1995.

[3]
Juselius, K. *The Cointegrated VAR Model*. Oxford: Oxford University Press, 2006.

[4]
Lütkepohl, H. *New Introduction to Multiple Time Series Analysis*. Berlin: Springer, 2005.