Output {drcarlate}

Computes All the Estimators

Description

Output is an integrated function that computes all the estimates (including NA, TSLS, L, NL, F, NP, R) used in Jiang et al. (2022). See the paper for more details.

Usage

Output(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. The authors set iq = 0.05 in Jiang et al. (2022).

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 1x8 vector: (1) NA (2) LP (3) LG (4) F (5) NP (6) R (when dgp = 3) (7) 2SLS (8) R (when dgp = 1 or 2). vsighat is a 1x8 vector: unscaled standard errors for vtauhat. vstat is a 1x8 vector: test statistic. vdeci is a 1x8 logical vector: if applicable, 1 means rejecting the null. 0 means not rejecting the null.

References

Jiang L, Linton O B, Tang H, Zhang Y. Improving estimation efficiency via regression-adjustment in covariate-adaptive randomizations with imperfect compliance [J]. 2022.

Examples

Output(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)