Description Usage Arguments Value References See Also Examples

Estimate two probit models with bivariate normally distributed error terms. This command still works if the first-stage dependent variable is not a regressor in the second stage.

1 2 3 4 5 6 7 8 9 |

`form1` |
Formula for the first probit model |

`form2` |
Formula for the second probit model |

`data` |
Input data, a data frame |

`par` |
Starting values for estimates |

`method` |
Optimization algorithm. Default is BFGS |

`verbose` |
Level of output during estimation. Lowest is 0. |

`accu` |
1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See optim |

A list containing the results of the estimated model

Peng, Jing. (2022) Identification of Causal Mechanisms from Randomized Experiments: A Framework for Endogenous Mediation Analysis. Information Systems Research (Forthcoming), Available at SSRN: https://ssrn.com/abstract=3494856

Other endogeneity:
`bilinear()`

,
`biprobit_latent()`

,
`biprobit_partial()`

,
`pln_linear()`

,
`pln_probit()`

,
`probit_linear_latent()`

,
`probit_linear_partial()`

,
`probit_linear()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |

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.