354—Appendix B. Command Reference
“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. |
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Examples
eq1.grads(n=out)
creates a group named OUT containing series named GRAD01, GRAD02, and GRAD03.
eq1.grads(n=out) g1 g2 g3
creates the same group, but names the series G1, G2 and G3.
Cross-references
See also derivs (p. 273), makederivs (p. 351), grads (p. 315).
makegraph |
Model Proc |
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Make graph object showing model series. |
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Syntax |
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Model Proc: |
model_name.makegraph(options) graph_name model_vars |
where graph_name is the name of the resulting graph object, and models_vars are the names of the series. The list of model_vars may include the following special keywords:
@all |
All model variables. |
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@endog |
All endogenous model variables. |
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@exog |
All exogenous model variables. |
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@addfactor |
All add factor variables in the model. |
Options
a |
Include actuals. |
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cInclude comparison scenarios.
dInclude deviations.
nDo not include active scenario (by default the active scenario is included).
makegroup—355
t= trans_type Transformation type: “level” (display levels in graph, (default=level) “pch” (display percent change in graph), “pcha” (display percent change - annual rates - in graph), “pchy” (display 1-year percent change in graph), “dif” (display
1-period differences in graph), “dify” (display 1-year differences in graph).
s=sol_type Solution type: “d” (deterministic), “m” (mean of sto- (default=“d”) chastic), “s” (mean and ±2 std. dev. of stochastic), “b”
(mean and confidence bounds of stochastic).
g=grouping Grouping setting for graphs: “v” (group series in graph (default=“v”) by model variable), “s” (group series in graph by scenario), “u” (ungrouped - each series in its own graph).
Examples
mod1.makegraph(a) gr1 y1 y2 y3
creates a graph containing the model series Y1, Y2, and Y3 in the active scenario and the actual Y1, Y2, and Y3.
mod1.makegraph(a,t=pchy) gr1 y1 y2 y3
plots the same graph, but with data displayed as 1-year percent changes.
Cross-references
See “Displaying Data” on page 816 of the User’s Guide for details. See Chapter 26, “Models”, on page 775 of the User’s Guide for a general discussion of models.
See makegroup (p. 355).
makegroup |
Model Proc | Pool Proc |
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Make a group out of pool and ordinary series using a pool object, or make a group out of model series and display dated data table.
Syntax
Pool Proc: |
pool_name.makegroup(group_name) pool_series1 [pool_series2 |
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pool_series3 …] |
Model Proc: |
model_name.makegroup(options) grp_name model_vars |
When used as a pool proc, you should provide a name for the new group in parentheses, then list the ordinary and pool series to be placed in the group.
356—Appendix B. Command Reference
When used as a model proc, the makegroup keyword should be followed by options, the name of the destination group, and the list of model variables to be created. The options control the choice of model series, and transformation and grouping features of the resulting dated data table view. The list of model_vars may include the following special keywords:
@all |
All model variables. |
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@endog |
All endogenous model variables. |
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@exog |
All exogenous model variables. |
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@addfactor |
All add factor variables in the model. |
Options
For Model Proc
a |
Include actuals. |
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cInclude comparison scenarios.
dInclude deviations.
nDo not include active scenario (by default the active scenario is included).
t= arg |
Transformation type: “level” (display levels), “pch” |
(default=level) |
(percent change), “pcha” (display percent change - |
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annual rates), “pchy” (display 1-year percent change), |
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“dif” (display 1-period differences), “dify” (display 1- |
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year differences). |
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s=arg |
Solution type: “d” (deterministic), “m” (mean of sto- |
(default=“d”) |
chastic), “s” (mean and ±2 std. dev. of stochastic), “b” |
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(mean and confidence bounds of stochastic). |
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g=arg |
Grouping setting for graphs: “v” (group series in graph |
(default=“v”) |
by model variable), “s” (group series in graph by sce- |
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nario). |
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Examples
pool1.makegroup(g1) x? z y?
places the ordinary series Z, and all of the series represented by the pool series X? and Y?, in the group G1.
model1.makegroup(a,n) group1 @endog
places all of the actual endogenous series in the group GROUP1.
makemap—357
Cross-references
See “Displaying Data” on page 816 of the User’s Guide for details. See Chapter 26, “Models”, on page 775 of the User’s Guide for a general discussion of models.
See also makegraph (p. 354).
makelimits |
Equation Proc |
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Create vector of limit points from ordered models.
makelimits creates a vector of the estimated limit points from equations estimated by ordered (p. 378).
Syntax
Equation Proc: |
eq_name.makelimits [vector_name] |
Provide a name for the vector after the makelimits keyword. If you do not provide a name, EViews will name the vector with the next available name of the form LIMITS## (if LIMITS01 already exists, it will be named LIMITS02, and so on).
Examples
equation eq1.ordered edu c age race gender
eq1.makelimit cutoff
Estimates an ordered probit and saves the estimated limit points in a vector named CUTOFF.
Cross-references
See “Ordered Dependent Variable Models” on page 636 of the User’s Guide for a discussion of ordered models.
makemap |
Alpha Series |
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Create numeric classification series and valmap from alpha series.
Syntax
Alpha Proc: |
alpha_name.makemap(options) ser_name map_name |
creates a classification series ser_name and an associated valmap map_name in the workfile. The valmap will automatically be assigned to the series.