bndovb: bndovb

In bndovb: Bounding Omitted Variable Bias Using Auxiliary Data

Description

This function runs a two sample least squares when auxiliary data contains every right-hand side regressor and main data contains a dependent variable and every right-hand side regressor but one omitted variable.

Usage

bndovb(
  maindat,
  auxdat,
  depvar,
  ovar,
  comvar,
  method = 1,
  mainweights = NULL,
  auxweights = NULL,
  signres = NULL
)

Arguments

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
ovar	A name of an omitted variable in main dataset which exists in auxiliary data
comvar	A vector of the names of common regressors existing in both main data and auxiliary data
method	CDF and Quantile function estimation method. Users can choose either 1 or 2. If the method is 1, the CDF and quantile function is estimated assuming a parametric normal distribution. If the method is 2, the CDF and quantile function is estimated using a nonparaemtric estimator in Li and Racine(2008) doi: 10.1198/073500107000000250, Li, Lin, and Racine(2013) doi: 10.1080/07350015.2012.738955. Default is 1.
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'.
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.

Value

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]

Author(s)

Yujung Hwang, [e-mail]

References

Hwang, Yujung (2021)

Bounding Omitted Variable Bias Using Auxiliary Data. Available at SSRN.doi: 10.2139/ssrn.3866876

Examples

data(maindat_nome)
data(auxdat_nome)

bndovb(maindat=maindat_nome,auxdat=auxdat_nome,depvar="y",ovar="x1",comvar=c("x2","x3"),method=1)

bndovb documentation built on July 31, 2021, 1:06 a.m.