Sspace
State space object. Estimation and evaluation of state space models using the Kalman filter.
Sspace Declaration
To declare a sspace object, use the sspace keyword, followed by a valid name.
Sspace Method
ml maximum likelihood estimation or filter initialization.
Sspace Views
cellipse Confidence ellipses for coefficient restrictions.
coefcov coefficient covariance matrix.
display display table, graph, or spool in object window.
endog table or graph of actual signal variables.
grads examine the gradients of the log likelihood.
label label information for the state space object.
output table of estimation results.
residcor standardized one-step ahead residual correlation matrix.
residcov standardized one-step ahead residual covariance matrix.
resids one-step ahead actual, fitted, residual graph.
results table of estimation and filter results.
spec text representation of state space specification.
statefinal display the final values of the states or state covariance.
stateinit display the initial values of the states or state covariance.
structure examine coefficient or variance structure of the specification.
wald Wald coefficient restriction test.
Sspace Procs
append add line to the specification.
clearhist clear the contents of the history attribute.
copy creates a copy of the sspace.
forecast perform state and signal forecasting.
makeendog make group containing actual values for signal variables.
makegrads make group containing the gradients of the log likelihood.
makemodel make a model object containing equations in sspace.
makesignals make group containing signal and residual series.
olepush push updates to OLE linked objects in open applications.
setattr set the value of an object attribute.
Sspace Data Members
Scalar Values
@coefcov(i,j) covariance of coefficients i and j.
@coefs(i) coefficient i.
@eqncoef(k) number of estimated coefficients in equation k.
@eqregobs(k) number of observations in signal equation k.
@linecount scalar containing the number of lines in the Sspace object.
@sddep(k) standard deviation of the signal variable in equation k.
@ssr(k) sum-of-squared standardized one-step ahead residuals for equation k.
@stderrs(i) standard error for coefficient i.
@tstats(i) t-statistic value for coefficient i.
Scalar Values (system level data)
@aic Akaike information criterion for the system.
@hq Hannan-Quinn information criterion for the system.
@logl value of the log likelihood function.
@ncoefs total number of estimated coefficients in the system.
@neqns number of equations for observable variables.
@regobs number of observations in the system.
@sc Schwarz information criterion for the system.
@totalobs sum of “@eqregobs” from each equation.
Vectors and Matrices
@coefcov covariance matrix for coefficients of equation.
@coefs coefficient vector.
@final_state matrix of final states.
@final_statecov (sym) covariance matrix of final state covariances.
@init_state matrix of initial states.
@init_statecov (sym) covariance matrix of initial state covariances.
@pvals vector containing the coefficient probability values.
@residcov (sym) covariance matrix of the residuals.
@stderrs vector of standard errors for coefficients.
@tstats vector of t-statistic values for coefficients.
State and Signal Results
The following functions allow you to extract the filter and smoother results for the estimation sample and place them in matrix objects. In some cases, the results overlap those available thorough the sspace procs, while in other cases, the matrix results are the only way to obtain the results.
Note also that since the computations are only for the estimation sample, the one-step-ahead predicted state and state standard error values will not match the final values displayed in the estimation output. The latter are the predicted values for the first out-of-estimation sample period.
@pred_signal matrix or vector of one-step ahead predicted signals.
@pred_signalcov matrix where every row is the @vech of the one-step ahead predicted signal covariance.
@pred_signalse matrix or vector of the standard errors of the one-step ahead predicted signals.
@pred_err matrix or vector of one-step ahead prediction errors.
@pred_errcov matrix where every row is the @vech of the one-step ahead prediction error covariance.
@pred_errcovinv matrix where every row is the @vech of the inverse of the one-step ahead prediction error covariance.
@pred_errse matrix or vector of the standard errors of the one-step ahead prediction errors.
@pred_errstd matrix or vector of standardized one-step ahead prediction errors.
@pred_state matrix or vector of one-step ahead predicted states.
@pred_statecov matrix where each row is the @vech of the one-step ahead predicated state covariance.
@pred_statese matrix or vector of the standard errors of the one-step ahead predicted states.
@pred_stateerr matrix or vector of one-step ahead predicted state errors.
@curr_err matrix or vector of filtered error estimates.
@curr_gain matrix or vector where each row is the @vec of the Kalman gain.
@curr_state matrix or vector of filtered states.
@curr_statecov matrix where every row is the @vech of the filtered state covariance.
@curr_statese matrix or vector of the standard errors of the filtered state estimates.
@sm_signal matrix or vector of smoothed signal estimates.
@sm_signalcov matrix where every row is the @vech of the smoothed signal covariance.
@sm_signalse matrix or vector of the standard errors of the smoothed signals.
@sm_signalerr matrix or vector of smoothed signal error estimates.
@sm_signalerrcov matrix where every row is the @vech of the smoothed signal error covariance.
@sm_signalerrse matrix or vector of the standard errors of the smoothed signal error.
@sm_signalerrstd matrix or vector of the standardized smoothed signal errors.
@sm_state matrix or vector of smoothed states.
@sm_statecov matrix where each row is the @vech of the smoothed state covariances.
@sm_statese matrix or vector of the standard errors of the smoothed state.
@sm_stateerr matrix or vector of the smoothed state errors.
@sm_stateerrcov matrix where each row is the @vech of the smoothed state error covariance.
@sm_stateerrse matrix or vector of the standard errors of the smoothed state errors.
@sm_stateerrstd matrix or vector of the standardized smoothed state errors.
@sm_crosserrcov matrix where each row is the @vec of the smoothed error cross-covariance.
String Values
@attr("arg") string containing the value of the arg attribute, where the argument is specified as a quoted string.
@command full command line form of the state space estimation command. Note this is a combination of @method and @options.
@description string containing the Sspace object’s description (if available).
@detailedtype returns a string with the object type: “SSPACE”.
@displayname returns the Sspace object’s display name. If the Sspace has no display name set, the name is returned.
@line(i) returns a string containing the i-th line of the Sspace object.
@name returns the Sspace’s name.
@options command line form of sspace estimation options.
@remarks string containing the sspace object’s remarks (if available).
@smpl sample used for estimation.
@svector returns an Svector where each element is a line of the Sspace object.
@svectornb same as @svector, with blank lines removed.
@type returns a string with the object type: “SSPACE”.
@updatetime returns a string representation of the time and date at which the Sspace was last updated.
Sspace Examples
The one-step-ahead state values and variances from ss01 may be saved using:
vector ss_state=ss01.@pred_state
matrix ss_statecov=ss01.@pred_statecov
Append a specification line to a sspace.
Syntax
sspace_name.append text
Type the text to be added after the append keyword.
Examples
vector(2) svec0=0
sspace1.append @mprior svec0
appends a line in the state space object SSPACE1 instructing EViews to use the zero vector SVEC0 as initial values for the state vector.
Cross-references
See
“Specifying a State Space Model in EViews” for a discussion of specification syntax.
Confidence ellipses for coefficient restrictions.
The cellipse view displays confidence ellipses for pairs of coefficient restrictions for an estimation object.
Syntax
sspace_name.cellipse(options) restrictions
Enter the object name, followed by a period, and the keyword cellipse. This should be followed by a list of the coefficient restrictions. Joint (multiple) coefficient restrictions should be separated by commas.
Options
ind=arg | Specifies whether and how to draw the individual coefficient intervals. The default is “ind=line” which plots the individual coefficient intervals as dashed lines. “ind=none” does not plot the individual intervals, while “ind=shade” plots the individual intervals as a shaded rectangle. |
size=number (default=0.95) | Set the size (level) of the confidence ellipse. You may specify more than one size by specifying a space separated list enclosed in double quotes. |
dist=arg | Select the distribution to use for the critical value associated with the ellipse size. The default depends on estimation object and method. If the parameter estimates are least-squares based, the  distribution is used; if the parameter estimates are likelihood based, the  distribution will be employed. “dist=f” forces use of the F-distribution, while “dist=c” uses the  distribution. |
prompt | Force the dialog to appear from within a program. |
p | Print the graph. |
Examples
The two commands:
s1.cellipse c(1), c(2), c(3)
s1.cellipse c(1)=0, c(2)=0, c(3)=0
both display a graph showing the 0.95-confidence ellipse for C(1) and C(2), C(1) and C(3), and C(2) and C(3).
s1.cellipse(dist=c,size="0.9 0.7 0.5") c(1), c(2)
displays multiple confidence ellipses (contours) for C(1) and C(2).
Cross-references
Clear the contents of the history attribute for sspace objects.
Removes the sspace’s history attribute, as shown in the label view of the sspace.
Syntax
sspace_name.clearhist
Examples
s1.clearhist
s1.label
The first line removes the history from the sspace S1, and the second line displays the label view of S1, including the now blank history field.
Cross-references
See
“Labeling Objects” for a discussion of labels and display names.
Clear the contents of the remarks attribute.
Removes the sspace’s remarks attribute, as shown in the label view of the sspace.
Syntax
sspace_name.clearremarks
Examples
s1.clearremarks
s1.label
The first line removes the remarks from the sspace S1, and the second line displays the label view of S1, including the now blank remarks field.
Cross-references
See
“Labeling Objects” for a discussion of labels and display names.
Coefficient covariance matrix.
Displays the covariances of the coefficient estimates for an estimated state space object.
Syntax
sspace_name.coefcov(options)
Options
p | Print the coefficient covariance matrix. |
Examples
ss1.coefcov
displays the coefficient covariance matrix for state space object SS1 in a window. To store the coefficient covariance matrix as a sym object, use “@coefcov”:
sym eqcov = ss1.@coefcov
Cross-references
Creates a copy of the sspace.
Creates either a named or unnamed copy of the sspace.
Syntax
sspace_name.copy
sspace_name.copy dest_name
Examples
s1.copy
creates an unnamed copy of the sspace S1.
s1.copy s2
creates S2, a copy of the sspace S1.
Cross-references
Display table, graph, or spool output in the sspace object window.
Display the contents of a table, graph, or spool in the window of the sspace object.
Syntax
sspace_name.display object_name
Examples
sspace1.display tab1
Display the contents of the table TAB1 in the window of the object SSPACE1.
Cross-references
Most often used in constructing an EViews Add-in. See
“Custom Object Output”.
Display name for state space objects.
Attaches a display name to a state space object which may be used to label output in place of the standard state space object name.
Syntax
sspace_name.displayname display_name
Display names are case-sensitive, and may contain a variety of characters, such as spaces, that are not allowed in state space object names.
Examples
ss1.displayname Hours Worked
ss1.label
The first line attaches a display name “Hours Worked” to the state space object SS1, and the second line displays the label view of SS1, including its display name.
Cross-references
See
“Labeling Objects” for a discussion of labels and display names.
Displays a spreadsheet or graph view of the endogenous variables.
Syntax
sspace_name.endog(options)
Options
g | Multiple line graphs of the solved endogenous series. |
p | Print the table of solved endogenous series. |
Examples
ss1.endog(g,p)
prints the graphs of the solved endogenous series.
Cross-references
Computes (n-period ahead) dynamic forecasts of the signals and states for an estimated state space.
forecast computes the forecast for all observations in a specified sample. In some settings, you may instruct forecast to compare the forecasted data to actual data, and to compute summary statistics.
Syntax
sspace_name.forecast(options) keyword1 names1 [keyword2 names2] [keyword3 names3] ...
You should enter a type-keyword followed by a list of names for the target series or a wildcard expression, and if desired, additional type-keyword and target pairs. The following are valid keywords: @state, @statese, @signal, @signalse. The first two keywords instruct EViews to forecast the state series and the values of the state standard error series. The latter two keywords instruct EViews to forecast the signal series and the values of the signal standard error series.
If a list is used to identify the targets, the number of target series must match the number of names implied by the keyword. Note that wildcard expressions may not be used for forecasting signal variables that contain expressions. In addition, the “*” wildcard expression may not be used for forecasting signal variables since this would overwrite the original data.
Options
i=arg (default=”o”) | State initialization options: “o” (one-step), “e” (EViews computed), “u” (user-specified), “s” (smoothed). |
m=arg (default=“d”) | Basic forecasting method: “n” (n-step ahead forecasting), “s” (smoothed forecasting), “d” (dynamic forecasting. |
mprior = vector_name | Name of state initialization (use if option “i=u” is specified). |
n=arg (default=1) | Number of n-step forecast periods (only relevant if n-step forecasting is specified using the method option). |
vprior= sym_name | Name of state covariance initialization (use if option “i=u” is specified). |
prompt | Force the dialog to appear from within a program. |
p | Print view. |
Examples
The following command performs n-step forecasting of the signals and states from a sspace object:
ss1.forecast(m=n,n=4) @state * @signal y1f y2f
Here, we save the state forecasts in the names specified in the sspace object, and we save the two signal forecasts in the series Y1F and Y2F.
Cross-references
State space forecasting is described in
“State Space Models and the Kalman Filter”. For additional discussion of wildcards, see
Appendix A. “Wildcards”.
Gradients of the objective function.
Displays the gradients of the objective function (where available) for an estimated sspace object.
The (default) summary form shows the value of the gradient vector at the estimated parameter values (if valid estimates exist) or at the current coefficient values. Evaluating the gradients at current coefficient values allows you to examine the behavior of the objective function at starting values. The tabular form shows a spreadsheet view of the gradients for each observation. The graphical form shows this information in a multiple line graph.
Syntax
sspace_name.grads(options)
Options
g | Display multiple graph showing the gradients of the objective function with respect to the coefficients evaluated at each observation. |
t (default) | Display spreadsheet view of the values of the gradients of the objective function with respect to the coefficients evaluated at each observation. |
p | Print results. |
Examples
To show a summary view of the gradients:
ss1.grads
To display and print the table view:
ss1.grads(t, p)
Cross-references
Display or change the label view of the state space object, including the last modified date and display name (if any).
As a procedure, label changes the fields in the state space object label.
Syntax
sspace_name.label
sspace_name.label(options) [text]
Options
The first version of the command displays the label view of the state space object. The second version may be used to modify the label. Specify one of the following options along with optional text. If there is no text provided, the specified field will be cleared.
c | Clears all text fields in the label. |
d | Sets the description field to text. |
s | Sets the source field to text. |
u | Sets the units field to text. |
r | Appends text to the remarks field as an additional line. |
p | Print the label view. |
Examples
The following lines replace the remarks field of SS1 with “Data from CPS 1988 March File”:
ss1.label(r)
ss1.label(r) Data from CPS 1988 March File
To append additional remarks to SS1, and then to print the label view:
ss1.label(r) Log of hourly wage
ss1.label(p)
To clear and then set the units field, use:
ss1.label(u) Millions of bushels
Cross-references
See
“Labeling Objects” for a discussion of labels.
Make a group out of the endogenous series.
Syntax
sspace_name.makeendog name
Following the keyword makeendog, you should provide a name for the group to hold the endogenous series. If you do not provide a name, EViews will create an untitled group.
Examples
ss1.makeendog grp_v1
creates a group named GRP_V1 that contains the endogenous series in SS1.
Cross-references
Create a “Kalman filter” sspace object.
Creates a new sspace object with all estimated parameter values substituted out of the specification. This procedure allows you to use the structure of the sspace without reference to estimated coefficients or the estimation sample.
Syntax
sspace_name.makefilter [filter_name]
If you provide a name for the sspace object in parentheses after the keyword, EViews will quietly create the named object in the workfile. If you do not provide a name, EViews will open an untitled sspace window if the command is executed from the command line.
Examples
ss1.makefilter kfilter
creates a new sspace object named KFILTER, containing the specification in SS1 with estimated parameter values substituted for coefficient elements.
Cross-references
Make a group containing individual series which hold the gradients of the objective function.
Syntax
sspace_name.makegrads(options) [ser1 ser2 ...]
The argument specifying the names of the series is also optional. If the argument is not provided, EViews will name the series “GRAD##” where ## is a number such that “GRAD##” is the next available unused name. If the names are provided, the number of names must match the number of target series.
Options
n=arg | Name of group object to contain the series. |
Examples
ss1.grads(n=out)
creates a group named OUT containing series named GRAD01, GRAD02, and GRAD03.
ss1.grads(n=out) g1 g2 g3
creates the same group, but names the series G1, G2 and G3.
Cross-references
Make a model from a state space object.
Syntax
sspace_name.makemodel(name) assign_statement
If you provide a name for the model in parentheses after the keyword, EViews will create the named model in the workfile. If you do not provide a name, EViews will open an untitled model window if the command is executed from the command line.
Examples
sspace.makemodel(sysmod) @prefix s_
makes a model named SYSMOD from the estimated system. SYSMOD includes an assignment statement “ASSIGN @PREFIX S_”. Use the command “show sysmod” or “sysmod.spec” to open the SYSMOD window.
Cross-references
See
“Models” for a discussion of specifying and solving models in EViews.
makeresids is no longer supported for the sspace object—see
Sspace::makesignals for more general replacement routines.
Generate signal series or signal standard error series from an estimated sspace object.
Options allow you to choose to generate one-step ahead and smoothed values for the signals and the signal standard errors.
Syntax
name.makesignals(options) [name_spec]
Follow the object name with a period and the makesignal keyword, options to determine the output type, and a list of names or wildcard expression identifying the series to hold the output. If a list is used to identify the targets, the number of target series must match the number of states implied in the sspace. If any signal variable contain expressions, you may not use wildcard expressions in the destination names.
Options
t=output_type (default=“pred”) | Defines output type: one-step ahead signal predictions (“pred”), RMSE of the one-step ahead signal predictions (“predse”, “residse”), error in one-step ahead signal predictions (“resid”), standardized one-step ahead prediction residual (“stdresid”), smoothed signals (“smooth”), RMSE of the smoothed signals (“smoothse”), estimate of the disturbances (“disturb”), RMSE of the estimate of the disturbances (“disturbse”), standardized estimate of the disturbances (“stddisturb”). |
n=group_name | Name of group to hold newly created series. |
prompt | Force the dialog to appear from within a program. |
Examples
ss1.makesignals(t=smooth) sm*
produces smoothed signals in the series with names beginning with “sm”, and ending with the name of the signal dependent variable.
ss2.makesignals(t=pred, n=pred_sigs) sig1 sig2 sig3
creates a group named PRED_SIGS which contains the one-step ahead signal predictions in the series SIG1, SIG2, and SIG3.
Cross-references
See
“State Space Models and the Kalman Filter” for details on state space models. For additional discussion of wildcards, see
Appendix A. “Wildcards”.
Generate state series or state standard error series from an estimated sspace object.
Options allow you to generate one-step ahead, filtered, or smoothed values for the states and the state standard errors.
Syntax
sspace_name.makestates(options) [name_spec]
Follow the object name with a period and the makestate keyword, options to determine the output type, and a list of names or a wildcard expression identifying the series to hold the output. If a list is used to identify the targets, the number of target series must match the number of names implied by the keyword.
Options
t=output_type (default=“pred”) | Defines output type: one-step ahead state predictions (“pred”), RMSE of the one-step ahead state predictions (“predse”), error in one-step ahead state predictions (“resid”), RMSE of the one-step ahead state prediction (“residse”), filtered states (“filt”), RMSE of the filtered states (“filtse”), standardized one-step ahead prediction residual (“stdresid”), smoothed states (“smooth”), RMSE of the smoothed states (“smoothse”), estimate of the disturbances (“disturb”), RMSE of the estimate of the disturbances (“disturbse”), standardized estimate of the disturbances (“stddisturb”). |
n=group_name | Name of group to hold newly created series. |
prompt | Force the dialog to appear from within a program. |
Examples
ss1.makestates(t=smooth) sm*
produces smoothed states in the series with names beginning with “sm”, and ending with the name of the state dependent variable.
ss2.makestates(t=pred, n=pred_states) sig1 sig2 sig3
creates a group named PRED_STATES which contains the one-step ahead state predictions in series SIG1, SIG2, and SIG3.
Cross-references
See
“State Space Models and the Kalman Filter” for details on state space models. For additional discussion of wildcards, see
Appendix A. “Wildcards”.
Maximum likelihood estimation of state space models.
Syntax
sspace_name.ml(options)
Options
optmethod = arg | Optimization method: “bfgs” (BFGS); “newton” (Newton-Raphson), “opg” or “bhhh” (OPG or BHHH), “legacy” (EViews legacy). BFGS is the default method. |
optstep = arg | Step method: “marquardt” (Marquardt); “dogleg” (Dogleg); “linesearch” (Line search). Marquardt is the default method. |
cov=arg | Covariance method: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich methods)., |
covinfo = arg | Information matrix method: “opg” (OPG); “hessian” (observed Hessian). (Applicable when non-legacy “optmethod=”.) |
b | Use Berndt-Hall-Hall-Hausman (BHHH) algorithm (default is Marquardt). |
m=integer | Set maximum number of iterations. |
c=scalar | Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coefficients. |
showopts / ‑showopts | [Do / do not] display the starting coefficient values and estimation options in the estimation output. |
prompt | Force the dialog to appear from within a program. |
p | Print basic estimation results. |
Examples
bvar.ml
estimates the sspace object BVAR by maximum likelihood.
Cross-references
See
“State Space Models and the Kalman Filter” for a discussion of user specified state space models.
Push updates to OLE linked objects in open applications.
Syntax
sspace_name.olepush
Cross-references
See
“Object Linking and Embedding (OLE)” for a discussion of using OLE with EViews.
Display estimation output.
output changes the default object view to display the estimation output (equivalent to using
Sspace::results).
Syntax
sspace_name.output
Options
p | Print estimation output for estimation object |
Examples
The output keyword may be used to change the default view of an estimation object. Entering the command:
ss1.output
displays the estimation output for state space object SS1.
Cross-references
Residual correlation matrix.
Displays the correlations of the residuals from each equation in the sspace object. The sspace object residuals used in the calculation are the standardized, one-step ahead signal forecast errors.
Syntax
sspace_name.residcor(options)
Options
p | Print the correlation matrix. |
Examples
ss1.residcor
displays the residual correlation matrix of sspace object SS1.
Cross-references
Residual covariance matrix.
Displays the covariances of the residuals from each equation in the sspace object. The sspace object residuals used in the calculation are the standardized, one-step ahead signal forecast errors.
Syntax
sspace_name.residcov(options)
Options
p | Print the covariance matrix. |
Examples
ss1.residcov
displays the residual covariance matrix of SS1.
Cross-references
Display residuals.
resids allows you to display and actual-fitted-residual graph using the one-step ahead estimates.
Syntax
sspace_name.resids(options)
Options
Examples
ss1.resids
displays a graph of the actual, fitted, and residual series for the sspace object SS1.
Cross-references
See
“State Space Models and the Kalman Filter” for a discussion of state space models.
Displays the results view of an estimated state space object.
Syntax
sspace_name.results(options)
Options
Examples
ss1.results(p)
displays and prints the results of the sspace object SS1.
Cross-references
See
“State Space Models and the Kalman Filter” for a discussion of state space models.
Set the object attribute.
Syntax
sspace_name.setattr(attr) attr_value
Sets the attribute attr to attr_value. Note that quoting the arguments may be required. Once added to an object, the attribute may be extracted using the @attr data member.
Examples
a.setattr(revised) never
String s = a.@attr("revised")
sets the “revised” attribute in the object A to the string “never”, and extracts the attribute into the string object S.
Cross-references
Graph signal series.
Display graphs of a set of signal series computed using the Kalman filter.
Syntax
sspace_name.signalgraphs(options)
Options
t=output_type (default=“pred”) | Defines output type:“pred” (one-step ahead signal predictions), “predse” (RMSE of the one-step ahead signal predictions), “resid” (error in one-step ahead signal predictions), “residse” (RMSE of the one-step ahead signal prediction; same as “predse”), “stdresid” (standardized one-step ahead prediction residual), “smooth” (smoothed signals), “smoothse” (RMSE of the smoothed signals), “disturb” (estimate of the disturbances), “disturbse” (RMSE of the estimate of the disturbances), “stddisturb” (standardized estimate of the disturbances). |
prompt | Force the dialog to appear from within a program. |
Examples
ss1.signalgraphs(t=smooth)
ss1.signalgraphs(t=smoothse)
displays a graph view containing the smoothed signal values, and then displays a graph view containing the root MSE of the smoothed states.
Cross-references
See
“State Space Models and the Kalman Filter” for a discussion of state space models.
Display the text specification view for sspace objects.
Syntax
sspace_name.spec(options)
Options
p | Print the specification text. |
Examples
ss1.spec
displays the specification of the sspace object SS1.
Cross-references
See
“Specifying a State Space Model in EViews” for a discussion of specification syntax.
Declare state space object.
Syntax
sspace sspace_name
Follow the sspace keyword with a name to be given the sspace object.
Examples
sspace stsp1
declares a sspace object named STSP1.
sspace tvp
tvp.append cs = c(1) + sv1*inc
tvp.append @state sv1 = sv1(-1) + [var=c(2)]
tvp.ml
declares a sspace object named TVP, specifies a time varying coefficient model, and estimates the model by maximum likelihood.
Cross-references
See
“State Space Models and the Kalman Filter” for a discussion of state space models.
Sspace::append may be used to add lines to an existing sspace object. See also
Sspace::ml for estimation of state space models.
Display final state values.
Show the one-step ahead state predictions or the state prediction covariance matrix at the final values
, where
is the last observation in the estimation sample. By default, EViews shows the state predictions.
Syntax
sspace_name.statefinal(options)
Options
c | Display the state prediction covariance matrix. |
p | Print the view. |
Examples
ss1.statefinal(c)
displays a view containing the final state covariances (the one-step ahead covariances for the first out-of-(estimation) sample period.
Cross-references
See
“State Space Models and the Kalman Filter” for a discussion of state space models.
Display graphs of a set of state series computed using the Kalman filter.
Syntax
sspace_name.stategraph(options)
Options
t=output_type (default=“pred”) | Defines output type:“pred” (one-step ahead signal predictions), “predse” (RMSE of the one-step ahead signal predictions), “resid” (error in one-step ahead signal predictions), “residse” (RMSE of the one-step ahead signal prediction; same as “predse”), “stdresid” (standardized one-step ahead prediction residual), “smooth” (smoothed signals), “smoothse” (RMSE of the smoothed signals), “disturb” (estimate of the disturbances), “disturbse” (RMSE of the estimate of the disturbances), “stddisturb” (standardized estimate of the disturbances). |
prompt | Force the dialog to appear from within a program. |
Other options
Examples
ss1.stategraphs(t=filt)
displays a graph view containing the filtered state values.
Cross-references
See
“State Space Models and the Kalman Filter” for a discussion of state space models.
Display initial state values.
Show the state initial values or the state covariance initial values used to initialize the Kalman Filter. By default, EViews shows the state values.
Syntax
sspace_name.stateinit(options)
Options
c | Display the covariance matrix. |
p | Print the view. |
Examples
ss1.stateinit
displays a view containing the initial state values (the one-step ahead predictions for the first period).
Cross-references
See
“State Space Models and the Kalman Filter” for a discussion of state space models.
Display summary of sspace specification.
Show view which summarizes the system transition matrices or the covariance structure of the state space specification. EViews can display either the formulae (default) or the values of the system transition matrices or covariance.
Syntax
sspace_name.structure(options) [argument]
If you choose to display the values for a time-varying system using the “v” option, you should use the optional [argument] to specify a single date at which to evaluate the matrices. If none is provided, EViews will use the first date in the current sample.
Options
v | Display the values of the system transition or covariance matrices. |
c | Display the system covariance matrix. |
p | Print the view. |
Examples
ss1.structure
displays a system transition matrices.
ss1.structure 1993q4
displays the transition matrices evaluated at 1993Q4.
Cross-references
See
“State Space Models and the Kalman Filter” for a discussion of state space models.
Update coefficient object values from state space object.
Copies coefficients from the sspace object into the appropriate coefficient vector or vectors in the workfile.
Syntax
sspace_name.updatecoefs
Follow the name of the sspace object by a period and the keyword updatecoefs.
Examples
ss1.updatecoefs
places the values of the estimated coefficients from SS1 in the coefficient vector in the workfile.
Cross-references
Wald coefficient restriction test.
The wald view carries out a Wald test of coefficient restrictions for a state space object.
Syntax
sspace_name.wald restrictions
Enter the sspace name, followed by a period, and the keyword. You must provide a list of the coefficient restrictions, with joint (multiple) coefficient restrictions separated by commas.
Options
Examples
ss1.wald c(2)=0, c(3)=0
tests the null hypothesis that the second and third coefficients in equation SS1 are jointly zero.
ss1.wald c(2)=c(3)*c(4)
tests the non-linear restriction that the second coefficient is equal to the product of the third and fourth coefficients.
Cross-references