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`disp( Mdl)` displays
summary information for the state-space model (

The software displays unknown parameter values using `c1` for
the first unknown parameter, `c2` for the second
unknown parameter, and so on.

For time-varying models, if there are more than 20 different sets of equations, then the software displays the first and last 10 groups in terms of time (the last group is the latest).

`disp( Mdl,params)` displays
the

`disp(___, Name,Value)` displays
the

The software always displays explicitly specified state-space models (that is, models you create without using a parameter-to-matrix mapping function). Try explicitly specifying state-space models first so that you can verify them using

`disp`.A parameter-to-matrix function that you specify to create

`Mdl`is a black box to the software. Therefore, the software might not display complex, implicitly defined state-space models.

A *state-space model* is
a discrete-time, stochastic model that contains two sets of equations:
one describing how a latent process transitions in time (the *state
equation*), and another describing how an observer measures
the latent process at each period (the *observation equation*).

Symbolically, you can write a linear, multivariate, Gaussian state-space model using the following system of equations

for *t* = 1,...,*T*.

, which is an

*m*-dimensional state vector describing the dynamics of some, possibly unobservable, phenomenon at period_{t}*t*., which is an

*n*-dimensional observation vector describing how the states are measured by observers at period_{t}*t*.*A*is the_{t}*m*-by-_{t}*m*_{t – 1}state-transition matrix describing how the states at time*t*transition to the states at period*t*– 1.*B*is the_{t}*m*-by-_{t}*k*state-disturbance-loading matrix describing how the states at period_{t}*t*combine with the innovations at period*t*.*C*is the_{t}*n*-by-_{t}*m*measurement-sensitivity matrix describing how the observations at period_{t}*t*relate to the states at period*t*.*D*is the_{t}*n*-by-_{t}*h*observation-innovation matrix describing how the observations at period_{t}*t*combine with the observation errors at period*t*.The matrices

*A*,_{t}*B*,_{t}*C*, and_{t}*D*are referred to as_{t}*coefficient matrices*, and might contain unknown parameters., which is a

*k*-dimensional, Gaussian, white-noise, unit-variance vector of state disturbances at period_{t}*t*., which is an

*h*-dimensional, Gaussian, white-noise, unit-variance vector of observation innovations at period_{t}*t*.*ε*and_{t}*u*are uncorrelated._{t}

To write a time-invariant state-space model, drop the *t* subscripts
of all coefficient matrices and dimensions.

If you implicitly created

`Mdl`using`ssm`, and if the software cannot infer locations for unknown parameters from the parameter-to-matrix function, then the software evaluates these parameters using their initial values and displays them as numerical values. For example, this happens when, for example, there is a random, unknown coefficient in the parameter-to-matrix function, which is a convenient form for a Monte Carlo study.The software always displays the initial state distributions as numerical values because, in many cases, the initial distribution depends on the values of the state equation matrices

`A`and`B`, which might be a complicated function of unknown parameters. In such situations, the software does not display the initial distribution symbolically, and, if`Mean0`and`Cov0`contain unknown parameters, then the software evaluates and displays numerical values for the unknown parameters.

[1] Durbin J., and S. J. Koopman. *Time Series
Analysis by State Space Methods*. 2nd ed. Oxford: Oxford
University Press, 2012.

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