Display quantile process coefficient estimates (multiple quantile regression estimates).
Syntax
eq_name.qrprocess(options) [arg] [@coefs coeflist]
where arg is a optional list containing the quantile values (specified using numbers, scalar objects, or vectors) for which you wish to compute estimates, and optionally the @coefs keyword followed by a coeflist of the subset of coefficients to display.
• If arg is not specified, EViews will display results for the original equation along with coefficients for equations estimated at a set of equally spaced number of quantiles as specified by the “n=” option. If “n=” is not specified, the default is to display results for the deciles.
• If arg is specified, EViews will display results for the original equation along with coefficients for equations estimated at the specified quantiles.
• If a coeflist is not provided, results for all coefficients will be displayed. For models that contain an intercept, the coeflist may consist of the @incptonly keyword, indicating that only results for the intercept will be displayed.
You may specify a maximum of 1000 total coefficients (number of display coefficients times the number of quantiles) and a maximum of 500 quantiles.
All estimation will be performed using the settings from the original equation.
Options
n=arg (default=10) | Number of quantiles for process estimates. |
graph | Display process estimate results as graph. |
size=arg (default=0.95) | Confidence interval size for graph display |
quantout=name | Save vector containing test quantile values. |
coefout=name | Save matrix containing test coefficient estimates. Each column of the matrix corresponds to a different quantile matching the corresponding quantile in “quantout=”. To match the covariance matrix given in “covout=” you should take the @vec of the coefficient matrix. |
covout=name | Save symmetric matrix containing covariance matrix for the vector set of coefficient estimates. |
prompt | Force the dialog to appear from within a program. |
p | Print output. |
Examples
equation eq1.qreg log(y) c log(x)
eq1.qrprocess
estimates a quantile (median) regression of LOG(Y) on a constant and LOG(X), and displays results for all nine quantiles in a table
Similarly,
equation eq1.qreg(quant=.4) log(y) c log(x)
eq1.qrprocess(coefcout=cout)
displays the coefficient estimated at the deciles (and at 0.4), and saves the coefficient matrix to COUT.
eq1.qrprocess(coefout=cout, n=4, graph)
eq1.qrprocess(coefout=cout, graph) .25 .5 .75
both estimate coefficients for the three quartiles and display the results in a graph, as does the equivalent:
vector v1(3)
v1.fill .25 .5 .75
eq1.qrprocess(graph) v1
Cross-references
See
“Process Coefficients” for a discussion of the quantile process.