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256—Appendix B. Command Reference

Examples

cor height weight age

displays a 3 by 3 correlation matrix for the three series HEIGHT, WEIGHT, and AGE.

group mygroup height weight age

mygroup.cor

displays the equivalent view using the group MYGROUP.

Cross-references

See also cov (p. 259), @cor (p. 614), and @cov (p. 614).

correl

Equation View | Group View | Series View | Var View

 

 

Display autocorrelation and partial correlations.

Displays the autocorrelation and partial correlation functions of the specified series, together with the Q-statistics and p-values associated with each lag.

When used with equation objects, correl displays the correlogram of the residuals of the equation.

Syntax

Object View: object_name.correl(n, options)

You must specify the largest lag n to use when computing the autocorrelations.

Options

p Print the correlograms.

Var View Options:

graph

Display correlograms (graphs).

 

 

byser

Display autocorrelations in tabular form, by series.

 

 

bylag

Display autocorrelations in tabular form, by lag.

 

 

Examples

m1.correl(24)

Displays the correlograms of the M1 series for up to 24 lags.

correlsq—257

Cross-references

See “Autocorrelations (AC)” on page 324 and “Partial Autocorrelations (PAC)” on page 325 of the User’s Guide for a discussion of autocorrelation and partial correlation functions, respectively.

See also correlsq (p. 257).

correlsq

Equation View

 

 

Correlogram of squared residuals.

Displays the autocorrelation and partial correlation functions of the squared residuals from an estimated equation, together with the Q-statistics and p-values associated with each lag.

Syntax

View:

equation_name.correl(n, options)

Options

nSpecify the number of lags of the correlograms to display.

p

Print the correlograms.

Examples

eq1.correl(24)

displays the correlograms of the squared residuals of EQ1 up to 24 lags.

Cross-references

See “Autocorrelations (AC)” on page 324 and “Partial Autocorrelations (PAC)” on page 325 of the User’s Guide for a discussion of autocorrelation and partial correlation functions, respectively.

See also correl (p. 256).

258—Appendix B. Command Reference

count

Command || Equation Method

 

 

Estimates models where the dependent variable is a nonnegative integer count.

Syntax

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

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

Follow the count keyword by the name of the dependent variable and a list of regressors.

Options

d=arg

Likelihood specification: Poisson likelihood (“p”), nor-

(default=”p”)

mal quasi-likelihood (“n”), exponential likelihood

 

(“e”), negative binomial likelihood or quasi-likelihood

 

(“b”).

 

 

v=positive_num

Specify fixed value for QML parameter in normal and

(default=1)

negative binomial quasi-likelihoods.

 

 

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 as the maximization

 

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.

 

 

s=number

Specify a number between zero and one to determine

 

starting values as a fraction of the EViews default val-

 

ues (out of range values are set to “s=1”).

 

 

cov—259

Examples

The command:

count(d=n,v=2,g) y c x1 x2

estimates a normal QML count model of Y on a constant, X1, and X2, with fixed variance parameter 2, and GLM standard errors.

equation eq1.count arrest c job police

eq1.makeresid(g) res_g

estimates a Poisson count model of ARREST on a constant, JOB, and POLICE, and stores the generalized residuals in the series RES_G.

equation eq1.count(d=p) y c x1

eq1.fit yhat

estimates a Poisson count model of Y on a constant and X1, and saves the fitted values (conditional mean) in the series YHAT.

equation eq1.count(d=p, h) y c x1

estimates the same model with QML standard errors and covariances.

Cross-references

See “Count Models” on page 656 of the User’s Guide for additional discussion.

cov

Command || Group View | Matrix View | Sym View

 

 

Compute covariance matrix.

Syntax

Command:

cov(options) ser1 ser2 [ser3 ...]

Group View:

group_name.cov(options)

Matrix View:

matrix_name.cov(options)

In command form, EViews will create an untitled group from the listed series or groups, then will display the covariance matrix view for that group. When used as a matrix view, cov displays the covariance matrix computed from the columns of the matrix.

Options

iCompute covariances using pairwise samples (default is to use the common sample).

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