STAMP 8.3 (April 2010)
- STAMP 8.3 works under
- The Ox code generator is introduced and fully supported by STAMP.
This new facility can generate
code for the model that is estimated in STAMP.
It complements the Batch code generator in STAMP. It is particularly useful
for those who use
for time series analysis in a production environment.
- The online help facility of STAMP is updated. In particular, the online help
for the Batch language and the new Ox code generator are rewritten.
- All weights and related computations in the Test/Weights dialog can be carried out,
also for time series with missing data.
- The Write forecasts option is combined with a Store forecasts option in the Test/Forecasting dialog.
The observations forecasts are stored after confirmation as a new variable with the forecasts attached
at the end of the sample. When necessary, the database sample is automatically
extended such that the forecast window is included.
The in-sample values of the new variable are the same as in the original series.
- The Edit/Save forecasts option in the Test/Forecasting dialog is reactivated
for model without explanatory variables.
- The Batch code options for Forecasting is extended; see Batch documentation.
- Variables and components in the Batch code need to be written between accolades. Specifically,
in the setcmp batch command we have "level", "slope", "seasonal", "cycle", "ar" and "irregular".
- Inclusion of lagged dependent variables is discouraged.
A new facility will be built in for the next version.
In this version it is best to treat and to have it as an exogenous variable.
- STAMP 8.2 works under
- The algorithm for the automatic detection of outliers and breaks
(option in the "Select components" dialog) is further advanced and
works more effectively.
- Output of variable names in "Regression effects in final state" is corrected
when Interventions are included in the model.
- Regression effects are computed correctly when
lagged explanatory variables are included in the model.
- Forecasting graphs are corrected for multivariate models
(no multiple lines in graph).
- Convergence criteria now depend on the number of
dependent variables in the model.
- Convergence criteria for EM method is adjusted for
certain model settings.
- Maximum likelihood estimation of the variance matrices
for the components is adjusted for
multivariate models when missing values are present.
- Solved error in regression output when using time-varying coefficients.
- Solved batch error when replicating current session of STAMP.
- Optimized automatic intervention selection (simultaneous for level and slope).
- Solved error with missing values in graphs for forecasting.
- Fixed forecasting mse with intervention in level.
- Fixed multiple cycle error in estimation.
- Added output for forecasts of components in model.
STAMP 8: new release !
Many new features have been introduced in version 8 of STAMP. The most
The multivariate structural time series model where the unobserved components become vectors and the disturbance variances
become disturbance variance matrices can be considered for the analysis of a
set of multiple time series. The number of multivariate options has
increased considerably compared to earlier versions of the program:
- Select components by equation: Different components can be
selected for different equations. This enables the user to analyse time
series with different dynamic characteristics jointly. For example, consider
two time series where one series may be subject to seasonal dynamics while
the other series does not require a seasonal component. The trends of the
two time series may move together. STAMP 8 allows the user to select a
seasonal component for the first series but not for the second series. This
applies to all components in STAMP: trend, seasonal, cycle, autoregressive, irregular, time-varying regressions, etc.
- Select regressions and interventions by equation: An option for selecting different
explanatory variables and interventions for different equations has been
available in STAMP versions 5 and 6. However, the current facility of
distributing explanatory variables over different equations has improved and is
- Design a dependence structure for each component:
Multivariate models in STAMP 5 and 6 were limited in their choice of
variance matrices: only full variance matrices of different ranks could be considered. A reduced-rank variance matrix
implies common features in multiple time series. This option remains in
STAMP but the specification has changed slightly. The disturbance variance
matrix imposes a dependence structure within the component vector (between
the different equations). This dependence can be designed by the user in a
simple way and for each component separately. For example, the cycle
component in equation 1 can be forced to depend on the cycles in equations 2
and 3 only.
- In STAMP 8 different variance matrices for different
disturbances can be chosen: The range of variance matrices includes scalar
and diagonal matrices, scaled matrices of ones (when applied to the slope
component, this implies balanced growth) and one rank plus diagonal
matrices. The latter case implies that a vector component can be decomposed
into common and idiosyncratic effects. In many applications, these different
specifications can be interpreted easily and can be highly interesting.
- The multivariate options extend to all models introduced in
STAMP 7: This includes the higher-order smooth trend models, the
higher-order (bandpass) cycle components and the (vector) autoregressive components of orders 1 and 2.
- Missing observations: They can also be handled within
multivariate time series models. This allows the interpolation of missing
observations through time but also through different time series.
- Forecasting of multivariate time series made simple: In particular,
STAMP 8 allows the incorporation of available future observations for the
explanatory variables in the database. Furthermore, future observations of
dependent variables are considered in graphical presentations of forecasts
and for the measurement of forecast accuracy (using standard measures such
as the root mean squared forecast error (RMSE) and the mean absolute
percentage error (MAPE).
- Estimation of parameters in multivariate time series models is
based on exact procedures: The diffuse initialisation of the Kalman filter is implemented, the exact likelihood function is computed and the score function
with respect to variance parameters is computed analytically and fast. This
leads to a robust estimation procedure in STAMP 8 and a relatively fast
- The number of graphical output for multivariate models is
increased: STAMP 8 offers an easy handling of the graphical output. An
option for graphics output selection for each equation is provided. The
powerful tools in OxMetrics 5 to edit graphical output are fully available
to STAMP 8 users.
Automatic outlier and break detection
Another major development in STAMP 8 is the
implementation of a new automatic detection procedure for outliers and
breaks in univariate and multivariate time series models. The following
features are available:
- STAMP 8 is able to propose a set of potential outliers and trend
breaks for univariate and multivariate time series. It is a basic but
effective two-step procedure based on the auxiliary residuals. First the
selected model is estimated and the diagnostics are investigated. Then a
first (larger) set of potential outliers and trend breaks are selected from
the auxiliary residuals. After re-estimation of the model, only those
interventions survive that are sufficiently significant. In the multivariate
case, this selection procedure is carried out jointly for each equation in
- After the automatic selection, the results are reported. All
considered outliers and breaks are kept in the intervention dialog and they
can be deleted from the model or added to the model in the usual way and
implemented as in STAMP 7. For future use, the interventions can be saved.
It prevents the manual input of outliers and breaks altogether.
- The automatic selection procedure can be repeated with the inclusion
of a fixed set of explanatory and intervention variables.
Other new features
- Each parameter in the models of STAMP 8 can be edited directly.
Parameters can be kept fixed at a particular value. Variances can be kept
fixed at values relative to a particular variance of another component (q-ratio). This facility also applies to multivariate models.
- General forecasting options have been extended and made more flexible. The
number of output options for prediction and forecasting have been increased.
Future values of explanatory variables available in the database can be used
for the forecasting of dependent variables.
- More output diagnostics are presented for predictions (one-step and
multi-step), auxiliary residuals and weight and gain functions.