bndovbme {bndovb} | R Documentation |

This function runs a two sample least squares when main data contains a dependent variable and every right hand side regressor but one omitted variable. The function requires an auxiliary data which includes every right hand side regressor but one omitted variable, and enough proxy variables for the omitted variable. When the omitted variable is continuous, the auxiliary data must contain at least two continuous proxy variables. When the omitted variable is discrete, the auxiliary data must contain at least three continuous proxy variables.

bndovbme( maindat, auxdat, depvar, pvar, ptype = 1, comvar, sbar = 2, mainweights = NULL, auxweights = NULL, normalize = TRUE, signres = NULL )

`maindat` |
Main data set. It must be a data frame. |

`auxdat` |
Auxiliary data set. It must be a data frame. |

`depvar` |
A name of a dependent variable in main dataset |

`pvar` |
A vector of the names of the proxy variables for the omitted variable. When proxy variables are continuous, the first proxy variable is used as an anchoring variable. When proxy variables are discrete, the first proxy variable is used for initialization (For details, see a documentation for "dproxyme" function). |

`ptype` |
Either 1 (continuous) or 2 (discrete). Whether proxy variables are continuous or discrete. Default is 1 (continuous). |

`comvar` |
A vector of the names of the common regressors existing in both main data and auxiliary data |

`sbar` |
A cardinality of the support of the discrete proxy variables. Default is 2. If proxy variables are continuous, this variable is irrelevant. |

`mainweights` |
An optional weight vector for the main dataset. The length must be equal to the number of rows of 'maindat'. |

`auxweights` |
An optional weight vector for the auxiliary dataset. The length must be equal to the number of rows of 'auxdat'. |

`normalize` |
Whether to normalize the omitted variable to have mean 0 and standard deviation 1. Set TRUE or FALSE. Default is TRUE. If FALSE, then the scale of the omitted variable is anchored with the first proxy variable in pvar list. |

`signres` |
An option to impose a sign restriction on a coefficient of an omitted variable. Set either NULL or pos or neg. Default is NULL. If NULL, there is no sign restriction. If 'pos', the estimator imposes an extra restriction that the coefficient of an omitted variable must be positive. If 'neg', the estimator imposes an extra restriction that the coefficient of an omitted variable must be negative. |

Returns a list of 4 components :

- hat_beta_l
lower bound estimates of regression coefficients

- hat_beta_u
upper bound estimates of regression coefficients

- mu_l
lower bound estimate of E[ovar*depvar]

- mu_u
upper bound estimate of E[ovar*depvar]

Yujung Hwang, yujungghwang@gmail.com

- Hwang, Yujung (2021)
Bounding Omitted Variable Bias Using Auxiliary Data. Available at SSRN. doi: 10.2139/ssrn.3866876

## load example data data(maindat_mecont) data(auxdat_mecont) ## set ptype=1 for continuous proxy variables pvar<-c("z1","z2","z3") cvar<-c("x","w1") bndovbme(maindat=maindat_mecont,auxdat=auxdat_mecont,depvar="y",pvar=pvar,ptype=1,comvar=cvar) ## set ptype=2 for discrete proxy variables data(maindat_medisc) data(auxdat_medisc) bndovbme(maindat=maindat_medisc,auxdat=auxdat_medisc,depvar="y",pvar=pvar,ptype=2,comvar=cvar)

[Package *bndovb* version 1.1 Index]