gt.bpm: Gradient test
In SemiParBIVProbit: Semiparametric Copula Regression Models

Description Usage Arguments Details Value WARNINGS Author(s) References See Also Examples

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.

gt.bpm(x)

`x`	A fitted `SemiParBIVProbit` object as produced by `SemiParBIVProbit()`.

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. (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.

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