factormodel (version 1.0)

cproxyme: cproxyme

Description

This function estimates a linear factor model using continuous variables. The linear factor model to estimate has the following form. proxy = intercept + factorloading * (latent variable) + measurement error The measurement error is assumed to follow a Normal distribution with a mean zero and a variance, which needs to be estimated.

Usage

cproxyme(dat, anchor = 1, weights = NULL)

Arguments

dat

A proxy variable data frame list.

anchor

This is a column index of an anchoring proxy variable. Default is 1. That is, the code will use the first column in dat data frame as an achoring variable.

weights

An optional weight vector

Value

Returns a list of 3 components :

alpha0

This is a vector of intercepts in a linear factor model. The k-th entry is the intercept of k-th proxy variable factor model.

alpha1

This is a vector of factor loadings. The k-th entry is the factor loading of k-th proxy variable. The factor loading of anchoring variable is normalized to 1.

varnu

This is a vector of variances of measurement errors in proxy variables. The k-th entry is the variance of k-th proxy measurement error. The measurement error is assumed to follow a Normal distribution with mean 0.

mtheta

This is a mean of the latent variable. It is equal to the mean of the anchoring proxy variable.

vartheta

This is a variance of the latent variable.

References

Cunha, F., Heckman, J. J., & Schennach, S. M. (2010)

Estimating the technology of cognitive and noncognitive skill formation. Econometrica, 78(3), 883-931. 10.3982/ECTA6551

Hwang, Yujung (2021)

Bounding Omitted Variable Bias Using Auxiliary Data. Working Paper.

Examples

# NOT RUN {
dat1 <- data.frame(proxy1=c(1,2,3),proxy2=c(0.1,0.3,0.6),proxy3=c(2,3,5))
cproxyme(dat=dat1,anchor=1)
## you can specify weights
cproxyme(dat=dat1,anchor=1,weights=c(0.1,0.5,0.4))

# }