Test your State-space knowledge

QuizTest your State-space knowledge

How much do you know?

Which of the following is NOT true about state-space models?

State-space models include an unobserved, or latent, component.

State-space models are a type of time series model.

There is no way to estimate the unobserved component of the model.

State-space models require specification of the relationship between the observed data and the unobserved component.

The advantages of state-space models include:

They can be used for linear or nonlinear systems.

They can be used for time-variant or time-invariant models.

Missing data can be handled naturally.

All of the above

State-space representation relates the unobserved model components to the observed data using:

A draw from a random distribution.

The measurement equation.

The transition/state equation.

None of the above.

All of the following are appropriate applications for state-space modeling except:

Survey data analysis.

Business cycles.

Stochastic volatility models.

Changes in global temperatures over time.

State-space models can capture:

Non-linear relationships.

Time-varying parameters.

Structural breaks.

All of the above.

State-space representation includes, but is not limited to:

A design, rotation, and selection matrix.

A transition, shifter, and selection matrix.

A design, transition, and selection matrix.

A transition, rotation, and selection matrix.

To estimate state-space models with unknown parameters we can use:

The Kalman filter alone.

Maximum likelihood estimation alone.

A combination of the Kalman filter and maximum likelihood estimation.

Ordinary linear regression.

There is always one unique state-space representation of a model.

True.

False.

The state-space representation is expressed in the

Logarithmic form.

Trigonometric form.

Matrix form.

Graphical form.

The transition, or state, equation:

Describes the relationship between the observed data and the unobserved components.

Represents the evolution of the unobserved component over time.

Is not used for estimation of state-space models.

Cannot contain unknown model parameters.

In the context of state-space models smoothing refers to:

Forecasting future values of the unobserved variable.

Estimating current values of the unobserved variable from past and current observations.

Estimating the past values of the unobserved variable given observed data.

None of the above.

Ready to put that knowledge to use and see how GAUSS can help you make an impact with state-space models? Sign-up today to get notified when our state-space library is available.

Your Information

Your first name

Your last name

Your email

Requester Role

Company / Institution

I'm interested in using state-space models for:

Resources

Manuals
Tutorials
White Papers
Product Flyers

User Forum

Join our community to see why our users are considered some of the most active and helpful in the
industry!

User Forum

Where To Buy

Available across the globe, you can have access to GAUSS no matter where you are.

Where to Buy
Request Pricing

Have a Specific Question?

Get a real answer from a real person

Need Support?

Get help from our friendly experts.

Contact Support