kClassIVreg.fit: Generic k-class estimator

In estsyawo/bayesprdopt: Useful Functions and Algorithms

Generic k-class estimator

Description

kClassIVreg.fit runs a generic linear IV model of the k-Class

Usage

kClassIVreg.fit(
  Y,
  X,
  Z,
  method = "JIVE",
  vctype = "HC3",
  cluster = NULL,
  weights = NULL
)

Arguments

Y	outcome variable
X	matrix of covariates.
Z	matrix of instruments. Defaults to X.
method	method of the k-class to implement. Defaults to "JIVE".
vctype	type of sandwich covariance matrix (see vcovHC)
cluster	vector of length n with cluster assignments of observations.
weights	a vector of length n of weights for observations

Details

Available methods in the k-Class include

"JIVE"

The Jackknife IV of Angrist et al. 1999

"LIML"

Limited Maximum Likelihood

"HLIM"

The Jackknife Limited Maximum Likelihood of Hausman et al. 2012

"HFUL"

The heteroskedasticity robust version of the Fuller (1977) estimator

Value

an IV regression object which also contains coefficients, standard errors, etc.

Examples

## Generate data and run MMD regression
n=200; set.seed(12); X = rnorm(n); er = rchisq(n,df=1)-1; Z=X 
Z=cbind(Z,Z^2,Z^3,Z^4);X=scale(abs(X))+er/sqrt(2); Y=X+er
summary(kClassIVreg.fit(Y=Y,X=X,Z=Z))
summary(ivreg::ivreg(formula = Y ~ X | Z)) #compare to conventional IV regression

estsyawo/bayesprdopt documentation built on April 2, 2024, 2:18 p.m.