The following product is developed by J. Dorfman, a third party developer, for use with GAUSS. Technical support is provided directly through the developer.
State Space Aoki Time Series
SSATS 2.0 is a set of preprogrammed GAUSS procedures that perform all the tasks necessary to and associated with the specification, estimation, and forecasting of multivariate state space time series models. A standard state space model takes the form:
yt = Cz t + et (observation equation)
zt+1 = Az t + Bet (state equation)
where yt is an (m x 1) vector of the time series to be modeled and/or forecast, z tis the (n x 1) state vector, e t is an (m x 1) vector of stochastic innovations (error terms), and A, B, and C are parameter matrices to be estimated.
Masanao Aoki developed a particularly successful algorithm to estimate such models based on the balanced representation and relying heavily on results from linear systems theory. SSATS 2.0 will let a researcher easily begin to implement the techniques laid out in Aoki's book, State Space Modeling of Time Series (Springer-Verlag, 1987, 1990).
SSATS will be useful to any researcher who is interested in empirical work on multivariate dynamic systems. SSATS is a valuable tool for anyone involved in the specification, estimation, and forecasting of multivariate (or univariate) time series models. The procedures can be used on their own, combined into a single command program, or used selectively in conjunction with other time series methods to aid in specification or forecast evaluation.
Platform: Windows, Mac, Linux
Requires: GAUSS Mathematical & Statistical System/GAUSS Engine v3.2 and above.