ivqr.ks: General Inference Testing general hypothesis regarding the...
In yuchang0321/IVQR: IVQR: Instrumental Variable Quantile Regression

Description Usage Arguments Value References Examples

General Inference Testing general hypothesis regarding the qunatile process of the endogenous variable.

1 2	ivqr.ks(object, variable = NULL, trim = c(0.05, 0.95), B = 2000, size = c(0.1, 0.05, 0.01, 0.001), nullH = "No_Effect", ...)

`object`	An ivqr object returned from the function `ivqr()`
`variable`	A number indicates which endogenous variable to test. Since at most two endongenous variables can be included in the function `ivqr`, this argument should be either 1 or 2.
`trim`	A vector of two numbers indicating the lower and upper bounds of the quantiles to consider.
`B`	Number of sub-sampling in the bootstrap. Default is 2000.
`size`	A vector indicates the desired size of the test. Critical values will be reported accordingly.
`nullH`	The null hypothesis to test. The four options are: No_Effect, Dominance, Location_Shift, and Exogeneity as defined in Chernozhukov and Hansen (2006).

An ivqr_ks object which contains information regarding test statistics, critical value, sub-sampling block size, ...etc.

Chernozhukov, V., & Hansen, C. (2006). Instrumental quantile regression inference for structural and treatment effect models. Journal of Econometrics, 132(2), 491-525.

data(ivqr_eg)
fit <- ivqr(y ~ d | z | x, seq(0.15,0.85,0.02), grid = seq(-2,2,0.2), data = ivqr_eg) # taus should be a fine grid 
ivqr(fit,nullH=No_Effect) # Test of no effect.
ivqr(fit,nullH=Dominance) # Test of dominance.
ivqr(fit,nullH=Location_Shift) # Test of location shift.
ivqr(fit,nullH=Exogeneity) # Test of exogeneity.