Variance_bounds {RegCombin}

Function to compute the variance bounds for Xnc

Description

Function to compute the variance bounds for Xnc

Usage

Variance_bounds(
  Ldata,
  Rdata,
  out_var,
  c_var,
  nc_var,
  constraint = NULL,
  c_sign = NULL,
  nc_sign = NULL,
  projections = TRUE,
  values,
  sam0,
  refs0,
  nb_pts,
  eps_default,
  nbCores,
  Bsamp = 2000,
  weights_x = NULL,
  weights_y = NULL,
  outside = FALSE,
  alpha = 0.05,
  values_sel = NULL,
  seed = 21
)

Arguments

Ldata

dataset containing (Y,Xc) where Y is the outcome, Xc are potential common regressors

Rdata

dataset containing (Xnc,Xc) where Xnc are the non commonly observed regressors, Xc are potential common regressors

out_var

label of the outcome variable Y.

c_var

label of the commonly observed regressors Xc.

nc_var

label of the non commonly observed regressors Xnc.

constraint

vector of the size of X_c indicating the type of constraint if any on f(X_c) : "monotone", "convex", "sign", or "none". Default is NULL, no contraints at all.

c_sign

sign restrictions on the commonly observed regressors: -1 for a minus sign, 1 for a plus sign, 0 otherwise. Default is NULL, i.e. no constraints.

nc_sign

sign restrictions on the non-commonly observed regressors Xnc: -1 for a minus sign, 1 for a plus sign, 0 otherwise. Default is NULL, i.e. no constraints.

projections

if FALSE compute the identified set along some directions or the confidence regions. Default is FALSE.

values

the different unique points of support of the common regressor Xc.

sam0

the directions q to compute the radial function.

refs0

indicating the positions in the vector values corresponding to the components of betac.

nb_pts

the constant C in DGM for the epsilon_0, the lower bound on the grid for epsilon, taken equal to nb_pts*ln(n)/n. Default is 1 without regressors Xc, 3 with Xc.

eps_default

If data_k =NULL, then epsilon is taken equal to eps_default.

nbCores

number of cores for the parallel computation. Default is 1.

Bsamp

the number of bootstrap/subsampling replications. Default is 1000.

weights_x

the sampling weights for the dataset (Xnc,Xc).

weights_y

the sampling weights for the dataset (Y,Xc).

outside

if TRUE indicates that the parallel computing has been launched outside of the function. Default is FALSE.

alpha

for the level of the confidence region. Default is 0.05.

values_sel

the selected values of Xc for the conditioning. Default is NULL.

seed

set a seed to fix the subsampling replications

Value

a list containing, in order: - ci : a list with all the information on the confidence intervals

- upper: upper bound of the confidence interval on betanc at level alpha, possibly with sign constraints

- lower: lower bound upper bound of the confidence interval on betanc, possibly with sign constraints

- unconstr: confidence interval on betanc, without sign constraints

- betac_ci: confidence intervals on each coefficients related to the common regressor, possibly with sign constraints

- betac_ci_unc: confidence intervals on each coefficients related to the common regressor without sign constraints

- point : a list with all the information on the point estimates

- upper: the upper bounds on betanc, possibly with sign constraints

- lower: the lower bounds on betanc, possibly with sign constraints

-unconstr: bounds on betanc without sign constraints

-betac_pt: bounds on betanc, possibly with sign constraints

-betac_pt_unc: bounds on betanc without sign constraints