IVselect {naivereg}

Selecting instrument variables using group lasso and B-splines in naivereg

Description

This shows which IVs are selected in the naivereg function.

Usage

IVselect(
  z,
  x,
  max.degree = 10,
  criterion = c("BIC", "AIC", "GCV", "AICc", "EBIC"),
  df.method = c("default", "active"),
  penalty = c("grLasso", "grMCP", "grSCAD", "gel", "cMCP"),
  endogenous.index = c(),
  IV.intercept = FALSE,
  family = c("gaussian", "binomial", "poisson")
)

Arguments

z

The instrument variables matrix.

x

The design matrix.

max.degree

The upper limit value of degree of B-splines when using BIC/AIC to choose the tuning parameters, default is BIC.

criterion

The criterion by which to select the regularization parameter. One of "AIC", "BIC", "GCV", "AICc","EBIC", default is "BIC".

df.method

How should effective model parameters be calculated? One of: "active", which counts the number of nonzero coefficients; or "default", which uses the calculated df returned by grpreg, default is "default".

penalty

The penalty to be applied to the model. For group selection, one of grLasso, grMCP, or grSCAD. For bi-level selection, one of gel or cMCP. Default is " grLasso".

endogenous.index

Specify which variables in design matrix are endogenous variables, the variable corresponds to the value 1 is endogenous variables, the variable corresponds to the value 0 is exogenous variable, default is all endogenous variables.

IV.intercept

Intercept of instrument variables, default is “FALSE”.

family

Either "gaussian" or "binomial", depending on the response, default is "gaussian".

Details

See naivereg.

Value

An object of type IVselect which is a list with the following components:

degree

Degree of B-splines.

criterion

The criterion by which to select the regularization parameter. One of "AIC", "BIC", "GCV", "AICc", "EBIC", default is "BIC".

ind

The index of selected instrument variables.

ind.b

The index of selected instrument variables after B-splines.

IVselect

The instrument variables after B-splines.

Author(s)

Qingliang Fan, KongYu He, Wei Zhong

References

Q. Fan and W. Zhong (2018), “Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective,” Journal of Business & Economic Statistics, doi: 10.1080/07350015.2016.1180991.

Caner, M. and Q. Fan (2015), Hybrid GEL Estimators: Instrument Selection with Adaptive Lasso, Journal of Econometrics, 187, 256–274.

Examples

#IV selecting with group Lasso an B-splines
library(naivereg)
data("naivedata")
x=naivedata[,1]
y=naivedata[,2]
z=naivedata[,3:22]
IV = IVselect(z,x)
IV$IVselect	#show the IV selected after B-splines