bndovb (version 1.1)

bndovbme: bndovbme

Description

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.

Usage

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

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.

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]

References

Hwang, Yujung (2021)

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

Examples

# NOT RUN {
## 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)

# }