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(To be removed) Generate `VARMAX`

model
responses from innovations

`vgxproc`

will be removed in a future release.
Use `filter`

instead.

[Y,logL] = vgxproc(Spec,W) [Y,logL] = vgxproc(Spec,W,X,Y0,W0)

`vgxproc`

generates model responses using
known innovations and a `VARMAX`

model specification.
To generate responses with simulated innovations, use `vgxsim`

.
To generate responses with zero-valued innovations, use `vgxpred`

.

`Spec` | A model specification structure for a multidimensional `VARMAX` time
series process, as produced by `vgxset` or `vgxvarx` . |

`W` | Innovations data, as produced by `vgxinfer` . `W` is
a matrix or a 3-D array. If `W` is a numObs-by-numDims matrix,
it represents numObs observations of a single path
of a numDims-dimensional time series. If `W` is
a numObs-by-numDims-by-numPaths array,
it represents numObs observations of numPaths paths
of a numDims-dimensional time series. Observations
across paths are assumed to occur at the same time. The last observation
is assumed to be the most recent. |

`X` | Exogenous data. `X` is a cell vector or a
cell matrix. Each cell contains a numDims-by-numX design
matrix `X(` so that, for some b,
`X(` *b is
the regression component of a single numDims-dimensional
observation `Y(` at time t.
If `X` is a numObs-by-1 cell vector,
it represents one path of the explanatory variables. If `X` is
a numObs-by-numXPaths cell matrix,
it represents numXPaths paths of the explanatory
variables. If `W` has multiple paths, `X` must
contain either a single path (applied to all paths in `W` )
or at least as many paths as in `W` (extra paths
are ignored). |

`Y0` | Presample response data. `Y0` is a matrix
or a 3-D array. If `Y0` is a numPresampleYObs-by-numDims matrix,
it represents numPresampleYObs observations of
a single path of a numDims-dimensional time series.
If `Y0` is a numPresampleYObs-by-numDims-by-numPreSampleYPaths array,
it represents numPresampleYObs observations of numPreSampleYPaths
paths of a numDims-dimensional time series. If `Y0` is
empty or if numPresampleYObs is less than the
maximum AR lag in Spec, presample values are padded with zeros. If numPresampleYObs is
greater than the maximum AR lag, the most recent samples from the
last rows of each path of `Y0` are used. If `W` has
multiple paths, `Y0` must contain either a single
path (applied to all paths in `W` ) or at least as
many paths as in `W` (extra paths are ignored). |

`W0` | Presample innovations data. `W0` is a matrix
or a 3-D array. If `W0` is a numPresampleWObs-by-numDims matrix,
it represents numPresampleWObs observations of
a single path of a numDims-dimensional time series.
If W0 is a numPresampleWObs-by-numDims-by-numPreSampleWPaths array,
it represents numPresampleWObs observations of numPreSampleWPaths
paths of a numDims-dimensional time series. If `W0` is
empty or if numPresampleWObs is less than the
maximum MA lag in Spec, presample values are padded with zeros. If numPresampleWObs is
greater than the maximum `MA` lag, the most recent
samples from the last rows of each path of `W0` are
used. If `W` has multiple paths, `W0` must
contain either a single path (applied to all paths in `W` )
or at least as many paths as in `W` (extra paths
are ignored). |

`Y` | Response data, the same size as `W` . |

`LogL` | 1-by-numPaths vector containing the total
loglikelihood of the response data in each path of `Y` . |

The functions `vgxinfer`

and `vgxproc`

are
complementary. For example, given a specification structure `Spec`

for
a stable and invertible process and an innovations process `W1`

,
the code

Y = vgxproc(Spec,W1,X,Y0,W0); W2 = vgxinfer(Spec,Y,X,Y0,W0);

`W2`

that is identical to `W1`

.
Differences can appear if the process in `Spec`

fails
to be either stable or invertible.Was this topic helpful?