gt.bpm: Gradient test
In GJRM: Generalised Joint Regression Modelling
gt.bpm R Documentation
Gradient test
Description
gt.bpm can be used to test the hypothesis of absence of endogeneity, correlated model equations/errors or non-random sample selection
in binary bivariate probit models.
Usage
gt.bpm(x)
Arguments
x
A fitted gjrm object.
Details
The gradient test was first proposed by Terrell (2002) and it is based on classic likelihood
theory. See Marra et al. (in press) for full details.
Value
It returns a numeric p-value corresponding to the null hypothesis that the correlation, \theta, is equal to 0.
WARNINGS
This test's implementation is only valid for bivariate binary probit models with normal errors.
Author(s)
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
References
Marra G., Radice R. and Filippou P. (2017), Regression Spline Bivariate Probit Models: A Practical Approach to Testing for Exogeneity. Communications in Statistics - Simulation and Computation, 46(3), 2283-2298.
Terrell G. (2002), The Gradient Statistic. Computing Science and Statistics, 34, 206-215.
Examples
## see examples for gjrm
GJRM documentation built on July 9, 2023, 7:15 p.m.
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.
| gt.bpm | R Documentation |
Gradient test
Description
gt.bpm can be used to test the hypothesis of absence of endogeneity, correlated model equations/errors or non-random sample selection
in binary bivariate probit models.
Usage
gt.bpm(x)
Arguments
x |
A fitted |
Details
The gradient test was first proposed by Terrell (2002) and it is based on classic likelihood theory. See Marra et al. (in press) for full details.
Value
It returns a numeric p-value corresponding to the null hypothesis that the correlation, \theta, is equal to 0.
WARNINGS
This test's implementation is only valid for bivariate binary probit models with normal errors.
Author(s)
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
References
Marra G., Radice R. and Filippou P. (2017), Regression Spline Bivariate Probit Models: A Practical Approach to Testing for Exogeneity. Communications in Statistics - Simulation and Computation, 46(3), 2283-2298.
Terrell G. (2002), The Gradient Statistic. Computing Science and Statistics, 34, 206-215.
Examples
## see examples for gjrm
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.