358—Appendix B. Command Reference
Options
nosort |
Do not sort the alpha series values in alphabetical order |
|
before assigning the map (default is to sort). |
|
|
Examples
stateabbrev.makemap statecodes statemap
creates a series STATECODES containing numeric coded values representing the states in STATEABBREV, and an associated valmap STATEMAP.
Cross-references
See “Alpha Series” on page 151 of the User’s Guide for a discussion of alpha series. See “Value Maps” on page 161 of the User’s Guide for a discussion of valmaps.
makemodel
Equation Proc | Logl Proc | Pool Proc | Sspace Proc | System Proc | Var Proc
Make a model from an estimation object.
Syntax
Object Proc: |
object_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
var var3.ls 1 4 m1 gdp tb3
var3.makemodel(varmod) @prefix s_
estimates a VAR and makes a model named VARMOD from the estimated var object. VARMOD includes an assignment statement “ASSIGN @PREFIX S_”. Use the command “show varmod” or “varmod.spec” to open the VARMOD window.
Cross-references
See Chapter 26, “Models”, on page 775 of the User’s Guide for a discussion of specifying and solving models in EViews.
See also append (p. 205), merge (p. 368) and solve (p. 475). For sspace objects, see also makefilter (p. 352).
makeresids—359
makeregs |
Equation Proc |
|
|
Make regressor group.
Creates a group containing the dependent and independent variables from an equation specification.
Syntax
Equation Proc: |
equation_name.makeregs grp_name |
Follow the keyword makeregs with the name of the group.
Examples
equation eq1.ls y c x1 x2 x3 z eq1.makeregs reggroup
creates a group REGGROUP containing the series Y X1 X2 X3 and Z.
Cross-references
See also group (p. 317) and equation (p. 286).
makeresids |
Equation Proc | Pool Proc | System Proc | Var Proc |
|
|
Create residual series.
Creates and saves residuals in the workfile from an object with an estimated equation or equations.
Syntax
Equation Proc: |
equation_name.makeresids(options) [res1] |
Object Proc: |
object_name.makeresids[res1 res2 res3] |
Pool Proc: |
pool_name.makeresids [poolser] |
Follow the object name with a period and the makeresids keyword, then provide a list of names to be given to the stored residuals. For pool residuals, you may use a cross section identifier “?” to specify a set of names.
For multiple equation objects, you should provide as many names as there are equations. If there are fewer names than equations, EViews creates the extra residual series with names RESID01, RESID02, and so on. If you do not provide any names, EViews will also name the residuals RESID01, RESID02, and so on.
360—Appendix B. Command Reference
[NOTE: makeresids is no longer supported for the sspace object— see makesignals (p. 361) for replacement routines].
Options
The following options are available only for the residuals from single equation objects:
o (default) |
Ordinary residuals. |
|
|
sStandardized residuals (available only after weighted estimation and GARCH, binary, ordered, censored, and count models).
g (default for |
Generalized residuals (available only for binary, |
ordered mod- |
ordered, censored, and count models). |
els) |
|
|
|
n=arg |
Create group object to hold the residual series. |
Examples
equation eq1.ls y c m1 inf unemp
eq1.makeresids res_eq1
estimates a linear regression of Y on a constant, M1, INF, UNEMP, and saves the residuals as a series named RES_EQ1.
var macro_var.ls 1 4 y m1 r
macro_var.makeresids resay res_m1 riser
estimates an unrestricted VAR with four lags and endogenous variables Y, M1, and R, and stores the residuals as RES_Y, RES_M1, RES_R.
equation probit1.binary y c x z probit1.makeresids(g) res_g series score1 = res_g
series score2 = res_g*x series score3 = res_g*z
estimates a probit model of Y on a constant, X, Z and stores the generalized residuals as RES_G. Then the vector of scores are computed as SCORE1, SCORE2, SCORE3.
pool1.makeresids res1_?
The residuals of each pool member will have a name starting with “RES1_” and the crosssection identifier substituted for the “?”.
makesignals—361
Cross-references
See “Weighted Least Squares” on page 467 of the User’s Guide for a discussion of standardized residuals after weighted least squares and Chapter 21, “Discrete and Limited Dependent Variable Models”, on page 619 of the User’s Guide for a discussion of standardized and generalized residuals in binary, ordered, censored, and count models.
For state space models, see makesignals (p. 361).
makesignals |
Sspace Proc |
|
|
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
Sspace Proc: |
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 Defines output type: one-step ahead signal predictions (default=pred) (“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.