| Title: | Bounding Omitted Variable Bias Using Auxiliary Data |
| Version: | 1.1 |
| Description: | Functions to implement a Hwang(2021) <doi:10.2139/ssrn.3866876> estimator, which bounds an omitted variable bias using auxiliary data. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 2.10) |
| RoxygenNote: | 7.1.1 |
| Imports: | np, pracma, stats, utils, MASS, dplyr, factormodel, nnet |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2021-07-27 01:03:15 UTC; yujung |
| Author: | Yujung Hwang 
[aut, cre] |
| Maintainer: | Yujung Hwang <yujungghwang@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2021-07-30 17:40:02 UTC |
A simulated auxiliary data to show how to use 'bndovbme' function with continuous proxy variables
Description
A simulated auxiliary data to show how to use 'bndovbme' function with continuous proxy variables
Usage
auxdat_mecont
Format
A data frame with 3000 rows and 5 variables:
- w1
A common covariate in both main and auxiliary data
- x
A common covariate in both main and auxiliary data
- z1
A continuous proxy variable
- z2
A continuous proxy variable
- z3
A continuous proxy variable
Source
This dataset was simulated by simulatePackageData.R in data-raw folder
A simulated auxiliary data to show how to use 'bndovbme' function with discrete proxy variables
Description
A simulated auxiliary data to show how to use 'bndovbme' function with discrete proxy variables
Usage
auxdat_medisc
Format
A data frame with 3000 rows and 5 variables:
- w1
A common covariate in both main and auxiliary data
- x
A common covariate in both main and auxiliary data
- z1
A discrete proxy variable
- z2
A discrete proxy variable
- z3
A discrete proxy variable
Source
This dataset was simulated by simulatePackageData.R in data-raw folder
A simulated auxiliary data to show how to use 'bndovb' function
Description
A simulated auxiliary data to show how to use 'bndovb' function
Usage
auxdat_nome
Format
A data frame with 50000 rows and 3 variables:
- x1
An omitted variable in the main data
- x2
A common covariate in both main and auxiliary data
- x3
A common covariate in both main and auxiliary data
Source
This dataset was simulated by simulatePackageData.R in data-raw folder
bndovb
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, yujungghwang@gmail.com
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)
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]
Author(s)
Yujung Hwang, yujungghwang@gmail.com
References
- Hwang, Yujung (2021)
Bounding Omitted Variable Bias Using Auxiliary Data. Available at SSRN. doi: 10.2139/ssrn.3866876
Examples
## 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)
A simulated main data to show how to use 'bndovbme' function with continuous proxy variables
Description
A simulated main data to show how to use 'bndovbme' function with continuous proxy variables
Usage
maindat_mecont
Format
A data frame with 3000 rows and 3 variables:
- w1
A common covariate in both main and auxiliary data
- x
A common covariate in both main and auxiliary data
- y
A dependent variable
Source
This dataset was simulated by simulatePackageData.R in data-raw folder
A simulated main data to show how to use 'bndovbme' function with discrete proxy variables
Description
A simulated main data to show how to use 'bndovbme' function with discrete proxy variables
Usage
maindat_medisc
Format
A data frame with 3000 rows and 3 variables:
- w1
A common covariate in both main and auxiliary data
- x
A common covariate in both main and auxiliary data
- y
A dependent variable
Source
This dataset was simulated by simulatePackageData.R in data-raw folder
A simulated main data to show how to use 'bndovb' function
Description
A simulated main data to show how to use 'bndovb' function
Usage
maindat_nome
Format
A data frame with 100000 rows and 3 variables:
- x2
A common covariate in both main and auxiliary data
- x3
A common covariate in both main and auxiliary data
- y
A dependent variable
Source
This dataset was simulated by simulatePackageData.R in data-raw folder