366—Appendix B. Command Reference
pool1.makesystem(name=sys2,cx=f) inv? @cxreg cap? @cxinst @trend inv?(-1)
This command creates a system named SYS2 with INV? as the dependent variable and a different intercept for each cross-section member equation. The regressor CAP? enters each equation with a different coefficient and each equation has two instrumental variables @TREND and INV? lagged.
Cross-references
See Chapter 23, “System Estimation”, on page 693 of the User’s Guide for a discussion of system objects in EViews.
map |
Alpha Proc | Series Proc |
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Assign or remove value map setting. |
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Syntax |
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Object Proc: |
series_name.map [valmap_name] |
If the optional valmap name is provided, the procedure will assign the specified value map to the series or alpha. If no name is provided, EViews will remove an existing valmap assignment.
Examples
alpha1.map mymap
assigns the valmap object MYMAP to the alpha series ALPHA1.
series1.map
removes an existing valmap assignment from SERIES1.
Cross-references
See “Value Maps” on page 161 of the User’s Guide for a discussion of valmap objects in EViews.
matrix |
Object Declaration |
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Declare and optionally initializes a matrix object.
Syntax
Command: matrix(r, c) matrix_name[=assignment]
means—367
The matrix keyword is followed by the name you wish to give the matrix. matrix also takes an optional argument specifying the row r and column c dimension of the matrix. Once declared, matrices may be resized by repeating the matrix command using the original name.
You may combine matrix declaration and assignment. If there is no assignment statement, the matrix will initially be filled with zeros.
You should use sym for symmetric matrices.
Examples
matrix mom
declares a matrix named MOM with one element, initialized to zero.
matrix(3,6) coefs
declares a 3 by 6 matrix named COEFS, filled with zeros.
Cross-references
See Chapter 3, “Matrix Language”, beginning on page 23 of the Command and Programming Reference for further discussion.
See “Matrix” (p. 168), “Rowvector” (p. 174) and “Vector” (p. 193) and “Sym” (p. 183) for full descriptions of the various matrix objects.
means |
Equation View |
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Descriptive statistics by category of dependent variable.
Computes and displays descriptive statistics of the explanatory variables (regressors) of an equation categorized by values of the dependent variable. means is currently available only for equations estimated by binary.
Syntax
Equation View: eq_name.means(options)
Options
p |
Print the descriptive statistics table. |
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Examples
equation eq1.binary(d=l) work c edu faminc
368—Appendix B. Command Reference
eq1.means
estimates a logit and displays the descriptive statistics of the regressors C, EDU, FAMINC for WORK=0 and WORK=1.
Cross-references
See Chapter 21, “Discrete and Limited Dependent Variable Models”, on page 619 of the User’s Guide for a discussion of binary dependent variable models.
merge |
Graph Proc | Model Proc |
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Merge objects.
When used as a model procedure, merges equations from an estimated equation, model, pool, system, or var object. When used as a graph procedure, merges graph objects.
If you supply only the object’s name, EViews first searches the current workfile for the object containing the equation. If the object is not found, EViews looks in the default directory for an equation or pool file (.DBE). If you want to merge the equations from a system file (.DBS), a var file (.DBV), or a model file (.DBL), include the extension in the command and an optional path when merging files. You must merge objects to a model one at a time; merge appends the object to the equations already existing in the model.
When used as a graph procedure, merge combines graph objects into a single graph object. The graph objects to merge must exist in the current workfile.
Syntax
Model Proc: |
model_name.merge(options) object_name |
Graph Proc: |
graph name.merge graph1 graph2 [graph3 ...] |
For use as a model procedure, follow the keyword with a name of an object containing estimated equation(s) to merge.
For use as a graph procedure, follow the keyword with a list of existing graph object names to merge.
Options
t |
Merge an ASCII text file (only for model merge). |
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Examples
eq1.makemodel(mod1)
mod1.merge eq2
ml—369
mod1.merge(t) c:\data\test.txt
The first line makes a model named MOD1 from EQ1. The second line merges (appends) EQ2 to MOD1 and the third line further merges (appends) the text file TEST from the specified directory.
graph mygra.merge gra1 gra2 gra3 gra4
show mygra.align(4,1,1)
The first line merges the four graphs GRA1, GRA2, GRA3, GRA4 into a graph named MYGRA. The second line displays the four graphs in MYGRA in a single row.
Cross-references
See Chapter 14, “Graphs, Tables, and Text Objects”, on page 413 of the User’s Guide for a discussion of graphs.
metafile |
Graph Proc |
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Save graph to disk as an enhanced or ordinary Windows metafile.
Provided for backward compatibility, metafile has been replaced by the more general graph proc save (p. 430), which allows for saving graphs in metafile or postscript files, with additional options for controlling the output.
ml |
Logl Method | Sspace Method |
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Maximum likelihood estimation of logl and state space models.
Syntax
Object Method: object_name.ml(options)
Options
bUse Berndt-Hall-Hall-Hausman (BHHH) algorithm (default is Marquardt).
m=integer |
Set maximum number of iterations. |
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c=scalar |
Set convergence criterion. The criterion is based upon |
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the maximum of the percentage changes in the scaled |
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coefficients. |
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showopts / |
[Do / do not] display the starting coefficient values and |
-showopts |
estimation options in the estimation output. |