408—Appendix B. Command Reference

Line graph.

Provided for backward compatibility. See line (p. 334).

Declare pool object.

Syntax

Command: pool name [id1 id2 id3 …]

Follow the pool keyword with a name for the pool object. You may optionally provide the identifiers for the cross-section members of the pool object. Pool identifiers may be added or removed at any time using add (p. 198) and drop (p. 281).

Examples

pool zoo1 dog cat pig owl ant

Declares a pool object named ZOO1 with the listed cross-section identifiers.

Cross-references

“Pool” on page 171 contains a a complete description of the pool object. See Chapter 27, “Pooled Time Series, Cross-Section Data”, on page 823 of the User’s Guide for a discussion of working with pools in EViews.

See add (p. 198) and drop (p. 281). See also ls (p. 345) for details on estimation using a pool object.

Prediction table for binary and ordered dependent variable models.

The prediction table displays the actual and estimated frequencies of each distinct value of the discrete dependent variable.

Syntax

Equation Proc: eq_name.predict(options)

print—409

For binary models, you may optionally specify how large the estimated probability must be to be considered a success (y = 1 ). Specify the cutoff level as an option in parentheses after the keyword predict.

Options

n (default=.5) Cutoff probability for success prediction in binary models (between 0 and 1).

Examples

equation eq1.binary(d=l) work c edu age race

eq1.predict(0.7)

Estimates a logit and displays the expectation-prediction table using a cutoff probability of 0.7.

Cross-references

See “Binary Dependent Variable Models” on page 619 of the User’s Guide for a discussion of binary models, and “Expectation-Prediction (Classification) Table” on page 627 of the User’s Guide for examples of prediction tables.

Sends views of objects to the default printer.

Syntax

Command: print(options) object1 [object2 object3 …]

Command: print(options) object_name.view_command

print should be followed by a list of object names or a view of an object to be printed. The list of names must be of the same object type. If you do not specify the view of an object, print will print the default view for the object.

Options

The default orientation is set by clicking on Print Setup.

410—Appendix B. Command Reference

Examples

print gdp log(gdp) d(gdp) @pch(gdp)

sends a table of GDP, log of GDP, first difference of GDP, and the percentage change of GDP to the printer.

print graph1 graph2 graph3

prints three graphs on a single page.

To merge the three graphs, realign them in one row, and print in landscape orientation, you may use the commands:

graph mygra.merge graph1 graph2 graph3

mygra.align(3,1,1)

print(l) mygra

To estimate the equation EQ1 and send the output view to the printer.

print eq1.ls gdp c gdp(-1)

Cross-references

See “Print Setup” beginning on page 941 of the User’s Guide for a discussion of print options and the Print Setup dialog.

See output (p. 380) for print redirection.

Estimation of binary dependent variable models with normal errors.

Equivalent to “binary(d=n)”.

See binary (p. 222).

program Command

Declare a program.

Syntax

Command: program [path\]prog_name

Enter a name for the program after the program keyword. If you do not provide a name, EViews will open an untitled program window. Programs are text files, not objects.

qqplot—411

Examples

program runreg

opens a program window named RUNREG which is ready for program editing.

Cross-references

See Chapter 6, “EViews Programming”, on page 83 of the Command and Programming Reference for further details, and examples of writing EViews programs.

See also open (p. 373).

Quantile-quantile plots.

Plots the (empirical) quantiles of a series against the quantiles of a theoretical distribution or the empirical quantiles of another series. You may specify the theoretical distribution and/or the method used to compute the empirical quantiles as options.

Syntax

ePlot against the quantiles of an exponential distribution.

xPlot against the quantiles of an extreme value distribution.