LM.bpm: Lagrange Multiplier Test (Score Test)

In GJRM: Generalised Joint Regression Modelling

Lagrange Multiplier Test (Score Test)

Description

Before fitting a bivariate probit model, LM.bpm can be used to test the hypothesis of absence of endogeneity, correlated model equations/errors or non-random sample selection.

Usage

LM.bpm(formula, data = list(), weights = NULL, subset = NULL, model, 
       hess = TRUE)

Arguments

formula	A list of two formulas, one for equation 1 and the other for equation 2. s terms are used to specify smooth smooth functions of predictors. Note that if model = "BSS" then the first formula MUST refer to the selection equation.
data	An optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula).
weights	Optional vector of prior weights to be used in fitting.
subset	Optional vector specifying a subset of observations to be used in the fitting process.
model	It indicates the type of model to be used in the analysis. Possible values are "B" (bivariate model) and "BSS" (bivariate model with sample selection). The two marginal equations have probit links.
hess	If FALSE then the expected (rather than observed) information matrix is employed.

Details

This Lagrange multiplier test (also known as score test) is used here for testing the null hypothesis that \theta is equal to 0 (i.e. no endogeneity, non-random sample selection or correlated model equations/errors, depending on the model being fitted). Its main advantage is that it does not require an estimate of the model parameter vector under the alternative hypothesis. Asymptotically, it takes a Chi-squared distribution with one degree of freedom. Full details can be found in Marra et al. (2014) and Marra et al. (2017).

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 [e-mail]

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.

Marra G., Radice R. and Missiroli S. (2014), Testing the Hypothesis of Absence of Unobserved Confounding in Semiparametric Bivariate Probit Models. Computational Statistics, 29(3-4), 715-741.

See Also

gjrm

Examples

## see examples for gjrm

GJRM documentation built on July 9, 2023, 7:15 p.m.