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502—Appendix B. Command Reference

testby

Series View

 

 

Test equality of the mean, median, or variance of a series across categories classified by a list of series or a group.

Syntax

Series View:

series_name.testby(options) arg1 [arg2 arg2 …]

Follow the testby keyword by a list of the names of the series or groups to use as classifiers. Specify the type of test as an option.

Options

mean (default)

Test equality of mean.

 

 

med

Test equality of median.

 

 

var

Test equality of variance.

 

 

dropna (default),

[Drop /Keep] NAs as a classification category.

keepna

 

 

 

v=integer

Bin categories if classification series take more than the

(default=100)

specified number of distinct values.

 

 

nov

Do not bin based on the number of values of the classi-

 

fication series.

 

 

a=number

Bin categories if average cell count is less than the spec-

(default=2)

ified number.

 

 

noa

Do not bin on the basis of average cell count.

 

 

b=integer

Set maximum number of binned categories.

(default=5)

 

 

 

p

Print the test results.

Examples

wage.testby(med) race

Tests equality of medians of WAGE across groups classified by RACE.

Cross-references

See “Equality Tests by Classification” on page 316 of the User’s Guide for a discussion of equality tests.

testdrop—503

See also testbtw (p. 501), teststat (p. 507).

testdrop

Command || Equation View | Pool View

 

 

Test whether to drop regressors from a regression.

Tests the hypothesis that the listed variables were incorrectly included in the estimated equation (only available for equations estimated by list). The test displays some combination of F and LR test statistics, as well as the test regression.

Syntax

Command: testdrop(options) arg1 [arg2 arg3 ...]

Object View:

eq_name.testdrop(options) arg1 [arg2 arg3 ...]

List the names of the series or groups of series to test for omission after the keyword. The command form applies the test to the default equation, if defined.

Options

p

Print output from the test.

 

 

Examples

ls sales c adver lsales ar(1)

testdrop adver

tests whether ADVER should be excluded from the specification for SALES. The commands:

equation oldeq.ls sales c adver lsales ar(1)

oldeq.testdrop adver

perform the same test using a named equation object.

pool1.testdrop(p) x?

drops X? from the existing pool specification and prints the results of the test.

Cross-references

See “Coefficient Tests” on page 568 of the User’s Guide for further discussion of testing coefficients.

See also testadd (p. 500) and wald (p. 530).

504—Appendix B. Command Reference

testexog

Var View

 

 

Perform exogeneity (Granger causality) tests on a VAR object.

Syntax

 

Var View:

var_name.testexog(options)

Options

 

 

 

 

 

name=arg

Save the Wald test statistics in named matrix object. See

 

 

below for a description of the statistics stored in the

 

 

matrix.

 

 

 

 

p

Print output from the test.

The name= option stores the results in a ( k + 1) × k matrix, where k is the number of endogenous variables in the VAR. In the first k rows, the i-th row, j-th column contains the Wald statistic for the joint significance of lags of the i-th endogenous variable in the j- th equation (note that the entries in the main diagonal are not reported in the table view). The degrees of freedom of the Wald statistics is the number of lags included in the VAR.

In the last row, the j-th column contains the Wald statistic for the joint significance of all lagged endogenous variables (excluding lags of the dependent variable) in the j-th equation. The degrees of freedom of the Wald statistics in the last row is ( k − 1 ) times the number of lags included in the VAR.

Examples

var var1.ls 1 6 lgdp lm1 lcpi

freeze(tab1) var1.testexog(name=exog)

The first line declares and estimates a VAR. The second line stores the exclusion test results in a named table TAB1, and stores the Wald statistics in a matrix named EXOG.

Cross-references

See “Diagnostic Views” on page 722 of the User’s Guide for a discussion of other VAR diagnostics.

See also testlags (p. 506).

testfit—505

testfit

Equation View

 

 

Carry out the Hosmer-Lemeshow and/or Andrews goodness-of-fit tests for estimated binary models.

Syntax

Equation View: binary_equation.testfit(options)

Options

hGroup by the predicted values of the estimated equation.

s=series_name

Group by the specified series.

 

 

integer

Specify the number of quantile groups in which to clas-

(default=10)

sify observations.

 

 

uUnbalanced grouping. Default is to randomize ties to balance the number of observations in each group.

vGroup according to the values of the reference series.

l=integer Limit the number of values to use for grouping. Should (default=100) be used with the “v” option.

p

Print the result of the test.

 

 

Examples

equation eq1.binary work c age edu

eq1.testfit(h,5,u)

estimates a probit specification, and tests goodness-of-fit by comparing five unbalanced groups of actual data to those estimated by the model.

Cross-references

See “Goodness-of-Fit Tests” on page 629 of the User’s Guide for a discussion of the Andrews and Hosmer-Lemeshow tests.

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