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**Class: **dssm

Refine initial parameters to aid diffuse state-space model estimation

`refine(Mdl,Y,params0)`

`refine(Mdl,Y,params0,Name,Value)`

`Output = refine(___)`

`refine(`

finds
a set of initial parameter values to use when fitting the state-space
model `Mdl`

,`Y`

,`params0`

)`Mdl`

to the response data `Y`

,
using the crude set of initial parameter values `params0`

.
The software uses several routines, and displays the resulting loglikelihood
and initial parameter values for each routine.

`refine(`

displays
results of the routines with additional options specified by one or
more `Mdl`

,`Y`

,`params0`

,`Name,Value`

)`Name,Value`

pair arguments. For example,
you can include a linear regression component composed of predictors
and an initial value for the coefficients.

Likelihood surfaces of state-space models can be complicated, for example, they can contain multiple local maxima. If

`estimate`

fails to converge, or converges to an unsatisfactory solution, then`refine`

can find a better set of initial parameter values to pass to`estimate`

.The refined initial parameter values returned by

`refine`

can appear similar to each other and to`params0`

. Choose a set yielding estimates that make economic sense and correspond to relatively large loglikelihood values.If a refinement attempt fails, then the software displays errors and sets the corresponding loglikelihood to

`-Inf`

. It also sets its initial parameter values to`[]`

.

The Kalman filter accommodates missing data by not updating
filtered state estimates corresponding to missing observations. In
other words, suppose that your data has a missing observation at period *t*.
Then, the state forecast for period *t*, based on
the previous *t* – 1 observations, is equivalent
to the filtered state for period *t*.

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