`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.

1 | ```
gt.bpm(x)
``` |

`x` |
A fitted |

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.

It returns a numeric p-value corresponding to the null hypothesis that the correlation, *θ*, is equal to 0.

This test's implementation is only valid for bivariate binary probit models with normal errors.

Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk

Marra G., Radice R. and Filippou P. (in press), Regression Spline Bivariate Probit Models: A Practical Approach to Testing for Exogeneity. *Communications in Statistics - Simulation and Computation*.

Terrell G. (2002), The Gradient Statistic. *Computing Science and Statistics*, 34, 206-215.

1 | ```
## see examples for SemiParBIVProbit
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.