tableRgAn: The result for the RSR method with respect to the robust g...
In ptest: Periodicity Tests in Short Time Series
Description
Usage
Format
References
Description
This data set is used to build up the response surface regressions
in order to obtain the p-values for the robust g test.
The simulation set-up of the response surface regression method is
i.i.d. statistics of length 10^5 with 200 replications and the
interpolated series sizes are c(8:19,seq(20,50,2),seq(55,100,5)).
Usage
1
Format
A list object with the following components:
model_matrix: A matrix with the estimated coefficents
for the Response Surface Regressions (one row per quantile).
nc: a vectpr of the lengths of the simulated series.
pc: A vector containing all probabilities at which
the quantiles are estimated.
r: the rate in the basis function g(n)=(1/n)^r.
q: the number of parameters (except the intercept) used in
the response surface regression, the intercept term is removed if negative.
References
MacKinnon, James (2001) :
Computing numerical distribution functions in
econometrics, Queen's Economics Department Working Paper, No. 1037.
ptest documentation built on May 2, 2019, 5:58 a.m.
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Description Usage Format References
Description
This data set is used to build up the response surface regressions in order to obtain the p-values for the robust g test. The simulation set-up of the response surface regression method is i.i.d. statistics of length 10^5 with 200 replications and the interpolated series sizes are c(8:19,seq(20,50,2),seq(55,100,5)).
Usage
1 |
Format
A list object with the following components:
model_matrix: A matrix with the estimated coefficents
for the Response Surface Regressions (one row per quantile).
nc: a vectpr of the lengths of the simulated series.
pc: A vector containing all probabilities at which
the quantiles are estimated.
r: the rate in the basis function g(n)=(1/n)^r.
q: the number of parameters (except the intercept) used in
the response surface regression, the intercept term is removed if negative.
References
MacKinnon, James (2001) : Computing numerical distribution functions in econometrics, Queen's Economics Department Working Paper, No. 1037.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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