bdstest

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bdstest—221

bar(l, x, o=mybar1) pop gdp cons

The bar graph is scaled on the left, while the line graphs are scaled on the right. The graph uses options from graph MYBAR1 as a template.

group mygrp oldsales newsales

mygrp.bar(s)

The first line defines a group of series and the second line displays a stacked bar graph view of the series in the group.

mygrp.bar(o=midnight, b)

creates an bar graph of the group G1, using the settings of the predefined template “midnight”, applying the bold modifier.

Cross-references

See “Graph Templates” on page 422 of the User’s Guide for a discussion of graph templates.

See graph (p. 316) for graph declaration and additional graph types.

bdstest	Series View

Perform BDS test for independence.

The BDS test is a Portmanteau test for time-based dependence in a series. The test may be used for testing against a variety of possible deviations from independence, including linear dependence, non-linear dependence, or chaos.

Syntax

	Series View:	series_name.bds(options)
Options

	m=arg	Method for calculating : “p” (fraction of pairs), “v”
	(default=“p”)	(fixed value), “s” (standard deviations), “r” (fraction of
		range).

	e=number	Value for calculating .

	d=integer	Maximum dimension.

	b=integer	Number of repetitions for bootstrap p-values. If option
		is omitted, no bootstraping is performed.

222—Appendix B. Command Reference

o=arg	Name of output vector for final BDS z-statistics.

p	Print output.

Cross-references

See “BDS Test” on page 327 of the User’s Guide for additional discussion.

binary	Command \|\| Equation Method

Estimate binary dependent variable models.

Estimates models where the binary dependent variable Y is either zero or one (probit, logit, gompit).

Syntax

Command: binary(options) y x1 [x2 x3 ...]

Equation Method: eq_name.binary(options) y x1 [x2 x3 ...]

Options

d=arg	Specify likelihood: normal likelihood function, probit
(default=“n”)	(“n” ), logistic likelihood function, logit (“l”), Type I
	extreme value likelihood function, Gompit (“x”).

q (default)	Use quadratic hill climbing as the maximization algo-
	rithm.

r	Use Newton-Raphson as the maximization algorithm.

b	Use Berndt-Hall-Hall-Hausman (BHHH) for maximiza-
	tion algorithm.

h	Quasi-maximum likelihood (QML) standard errors.

g	GLM standard errors.

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.

s	Use the current coefficient values in C as starting val-
	ues.

block—223

s=number	Specify a number between zero and one to determine
	starting values as a fraction of EViews default values
	(out of range values are set to “s=1”).

showopts /	[Do / do not] display the starting coefficient values and
-showopts	estimation options in the estimation output.

p	Print results.

Examples

To estimate a logit model of Y using a constant, WAGE, EDU, and KIDS, and computing QML standard errors, you may use the command:

binary(d=l,h) y c wage edu kids

Note that this estimation uses the default global optimization options. The commands:

param c(1) .1 c(2) .1 c(3) .1

equation probit1.binary(s) y c x2 x3

estimate a probit model of Y on a constant, X2, and X3, using the specified starting values. The commands:

coef beta_probit = probit1.@coefs

matrix cov_probit = probit1.@coefcov

store the estimated coefficients and coefficient covariances in the coefficient vector BETA_PROBIT and matrix COV_PROBIT.

Cross-references

See “Binary Dependent Variable Models” on page 619 of the User’s Guide for additional discussion.

block	Model View

Display the model block structure view.

Show the block structure of the model, identifying which blocks are recursive and which blocks are simultaneous.

Syntax

Model View:

model_name.block(options)

224—Appendix B. Command Reference

Options

p	Print the block structure view.

Cross-references

See “Block Structure View” on page 796 of the User’s Guide for details. Chapter 26 of the User’s Guide provides a general discussion of models.

See also eqs (p. 287), text (p. 508) and vars (p. 529) for alternative representations of the model.

boxplot	Group View

Display boxplot of each series in the group.

Create a boxplot graph view containing boxplots for each series in the group.

Syntax

Group View:

group_name.boxplot(options)

Follow the group name with a period, the keyword, and any options. The default settings are to display fixed width boxplots for each series using individual samples, with all basic elements drawn (mean, med, staples, whiskers, near outliers, far outliers), and with shading representing the approximate confidence intervals for the median.

Options

Options to control initial display

nomean	Do not display means.

nomed	Do not display medians.

nostaple	Do not display staples.

nowhisk	Do not display whiskers.

nonearout	Do not display near outliers.

nofarout	Do not display far outliers.

width=arg	Boxplot width: “fixed” (fixed width boxplots), “n”
(default=	(width proportional to number of observations),
“fixed”)	“rootn” (width proportional to square root of number of
	observations).

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