MATLAB Examples

Smooth states of a time-invariant, state-space model that contains a regression component.

Estimate a regression model containing a regression component, and then forecast observations from the fitted model.

Fit a state-space model that has an observation-equation regression component.

Filter states of a time-invariant, state-space model that contains a regression component.

Create a stationary ARMA model subject to measurement error using ssm.

Estimate a random, autoregressive coefficient of a state in a state-space model. That is, this example takes a Bayesian view of state-space model parameter estimation by using the

Generate data from a known model, fit a state-space model to the data, and then simulate series from the fitted model.

Implicitly create a diffuse state-space model that contains a regression component in the observation equation. The state model contains an ARMA(1,1) state and random walk.

Generate data from a known model, fit a state-space model to the data, and then forecast states and observations states from the fitted model.

Generate data from a known model, fit a state-space model to the data, and then smooth the states.

Create a time-varying, state-space model by passing a parameter-mapping function describing the model to ssm (i.e., implicitly create a state-space model).

Generate data from a known model, fit a diffuse state-space model to the data, and then smooth the states.

How to:

Generate data from a known model, fit a diffuse state-space model to the data, and then filter the states.

Generate data from a known model, fit a diffuse state-space model to the data, and then forecast states and observations states from the fitted model.

Generate data from a known model, fit a state-space model to the data, and then filter the states.

Generates data from a known model, fits a state-space model to the data, and then simulates series from the fitted model using the simulation smoother.

Create a time-varying, state-space model containing a random, state coefficient.

Create a diffuse state-space model in which one of the state variables drops out of the model after a certain period.

Implicitly create a state-space model that contains a regression component in the observation equation. The state model is an ARMA(1,1).

Generate and visualize random walks through a Markov chain.

Compares the estimated mixing times of several Markov chains having differing structures. Convergence theorems typically require ergodic unichains. Therefore, before comparing

Create a Markov chain object to model a hypothetical economic cycle using a stochastic transition matrix.

Specify certain infeasible transitions and randomly distribute others within a transition matrix for a Markov chain.

Compute the stationary distribution of a Markov chain, estimate its mixing time, and determine whether the chain is ergodic and reducible. The example also shows how to remedy periodicity

Visualize the structure and evolution of a Markov chain model using the dtmc plotting functions. Consider the four-state Markov chain that models real GDP dynamics in

Compute and visualize state redistributions, which is the evolution of the deterministic state distributions over time from an initial distribution.

Create a Markov chain object from a matrix containing observed transitions rather than a stochastic transition matrix.

Programmatically and visually identify classes in a Markov chain. The example also extracts a recurrent class from the chain for further analysis.

Create a Markov chain object from a randomly generated, right-stochastic transition matrix. Such a Markov chain is convenient for exploration and testing.

Forecast a state-space model using Monte-Carlo methods, and to compare the Monte-Carlo forecasts to the theoretical forecasts.

How the results of the state-space model simulation smoother ( simsmooth ) compare to the smoothed states ( smooth ).

Forecast a time-varying, state-space model, in which there is a regime change in the forecast horizon.

Forecast observations of a known, time-invariant, state-space model.

Simulate states and observations of a known, time-invariant state-space model.

Create a time-invariant, state-space model containing known parameter values using ssm .

Smooth the states of a known, time-invariant, state-space model.

Filter states of a known, time-invariant, state-space model.

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