328—Appendix B. Command Reference
The last row contains statistics for the joint test. The second and fourth column of the
(k + 1) row is simply the sum of all the rows above in the corresponding column and are the χ2k statistics for the joint skewness and kurtosis tests, respectively. These joint skewness and kurtosis statistics add up to the joint Jarque-Bera statistic reported in the output table, except for the “factor=cov” option. When this option is set, the joint Jarque-Bera statistic includes all cross moments (in addition to the pure third and fourth moments). The overall Jarque-Bera statistic for this statistic is stored in the first column of the
(k + 1) row (which will be a missing value for all other options).
Examples
var var1.ls 1 6 lgdp lm1 lcpi
show var1.jbera(factor=cor,name=jb)
The first line declares and estimates a VAR. The second line carries out the residual multivariate normality test using the inverse square root of the residual correlation matrix as the factorization matrix and stores the results in a matrix named JB.
Cross-references
See Chapter 24, “Vector Autoregression and Error Correction Models”, on page 719 of the User’s Guide for a discussion of the test and other VAR diagnostics.
kdensity |
Series View |
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Kernel density plots. Displays nonparametric kernel density estimates of the specified series.
Syntax
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Series View: |
series_name.kdensity(options) |
Options |
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k=arg |
Kernel type: “e” (Epanechnikov), “r” (Triangular), “u” |
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(default=“e”) |
(Uniform), “n” (Normal–Gaussian), “b” (Biweight– |
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Quartic), “t” (Triweight), “c” (Cosinus). |
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s (default) |
Automatic bandwidth (Silverman). |
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b=number |
Specify a number for the bandwidth. |
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b |
Bracket bandwidth. |
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integer |
Number of points to evaluate. |
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(default=100) |
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kerfit—329
x |
Exact evaluation of kernel density. |
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o=arg |
Name of matrix to hold results of kernel density compu- |
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tation. The first column of the matrix contains the eval- |
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uation points and the remaining columns contain the |
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kernel estimates. |
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p |
Print the kernel density plot. |
Examples
lwage.kdensity(k=n)
plots the kernel density estimate of LWAGE using a normal kernel and the automatic bandwidth selection.
Cross-references
See “Kernel Density” on page 394 of the User’s Guide for a discussion of kernel density estimation.
kerfit |
Group View |
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Fits a kernel regression of the second series in the group (vertical axis) against the first series in the group (horizontal axis).
Syntax
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Group View: |
group_name.kerfit(options) |
Options |
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k=arg |
Kernel type: “e” (Epanechnikov), “r” (Triangular), “u” |
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(default=“e”) |
(Uniform), “n” (Normal–Gaussian), “b” (Biweight– |
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Quartic), “t” (Triweight), “c” (Cosinus). |
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b=number |
Specify a number for the bandwidth. |
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b |
Bracket bandwidth. |
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integer |
Number of grid points to evaluate. |
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(default=100) |
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x |
Exact evaluation of the polynomial fit. |
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d=integer |
Degree of polynomial to fit. Set “d=0” for Nadaraya- |
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(default=1) |
Watson regression. |
330—Appendix B. Command Reference
s=name |
Save fitted series. |
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p |
Print the kernel fit plot. |
Examples
group gg weight height
gg.kerfit(s=w_fit, 200)
Fits a kernel regression of HEIGHT on WEIGHT using 200 points and saves the fitted series as W_FIT.
Cross-references
See “Scatter with Kernel Fit” on page 403 of the User’s Guide for a discussion of kernel regression.
See also linefit (p. 337), nnfit (p. 372).
label |
Object View | Object Proc |
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Display or change the label view of the object, including the last modified date and display name (if any).
As a procedure, label changes the fields in the object label.
Syntax
Object View: |
object_name.label |
Object Proc: |
object_name.label(options) text |
Options |
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To modify the label, you should specify one of the following options along with optional text. If there is no text provided, the specified field will be cleared:
cClears all text fields in the label.
dSets the description field to text.
s |
Sets the source field to text. |
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u |
Sets the units field to text. |
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r |
Appends text to the remarks field as an additional line. |
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p |
Print the label view. |
laglen—331
Examples
The following lines replace the remarks field of LWAGE with “Data from CPS 1988 March File”:
lwage.label(r)
lwage.label(r) Data from CPS 1988 March File
To append additional remarks to LWAGE, and then to print the label view:
lwage.label(r) Log of hourly wage
lwage.label(p)
To clear and then set the units field, use:
lwage.label(u) Millions of bushels
Cross-references
See “Labeling Objects” on page 80 of the User’s Guide for a discussion of labels.
See also displayname (p. 276).
laglen |
Var View |
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VAR lag order selection criteria. |
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Syntax |
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Var View: |
var_name.laglen(m, options) |
You must specify the maximum lag order m for which you wish to test.
Options
vname=arg Save selected lag orders in named vector. See below for a description of the stored vector.
mname=arg Save lag order criteria in named matrix. See below for a description of the stored matrix.
p |
Print table of lag order criteria. |
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The “vname=” option stores a vector with 5 rows containing the selected lags from the following criteria: sequential modified LR test (row 1), final prediction error (row 2), Akaike information criterion (row 3), Schwarz information criterion (row 4), HannanQuinn information criterion (row 5).