ATEOutput {drcarlate}

Computes linear, nonparametric and regularized ATE estimator

Description

ATEOutput is the version of Output under full compliance.

Usage

ATEOutput(ii, tau, dgptype, rndflag, n, g, pi, iPert, iq, iridge)

Arguments

ii

Monte Carlo index.

tau

A scalar. The simulated true LATE effect.

dgptype

A Scalar. 1, 2, 3 (See Jiang et al. (2022) for DGP details).

rndflag

Method of CAR (covariate-adaptive randomizations). Its value can be 1, 2, 3 or4. 1-SRS; 2-WEI; 3-BCD; 4-SBR. See Jiang et al. (2022) for more details about CAR.

n

Sample size.

g

Number of strata. The authors set g=4 in Jiang et al. (2022).

pi

Targeted assignment probability across strata.

iPert

A scalar. iPert =0 means size. Otherwise means power: iPert is the perturbation of false null.

iq

Size of hypothesis testing. We set iq = 0.05.

iridge

A scalar. The penalization parameter in ridge regression.

Value

A list containing four matrices named vtauhat, vsighat, vstat and vdeci respectively. vtauhat is a 1x4 vector: (1) L (2) NL (3) R(dgp = 1 or 2) (4) R(dgp = 3). vsighat is a 1x4 vector: unscaled standard errors for vtauhat. vstat is a 1x4 vector: test statistic. vdeci is a 1x4 logical vector: if applicable, 1 means rejecting the null. 0 means not rejecting the null.

Examples

ATEOutput(ii = 1, tau = 0.9122762, dgptype = 1,
        rndflag = 4, n = 2000, g = 4, pi = c(0.5,0.5,0.5,0.5),
        iPert = 1, iq = 0.05, iridge = 0.001)