JRM (version 0.1-1)

gt.bpm: 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 SemiParBIV object as produced by SemiParBIV().

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.

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.

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.

See Also

SemiParBIV

Examples

# NOT RUN {
## see examples for SemiParBIV
# }