RegCombin (version 0.4.1)

point_ident_test: Function performing the test of point identification on a validation sample.

Description

Function performing the test of point identification on a validation sample.

Usage

point_ident_test(
  validation,
  Ldata = NULL,
  Rdata = NULL,
  out_var,
  nc_var,
  c_var = NULL,
  alpha = 0.05,
  constraint = NULL,
  nc_sign = NULL,
  c_sign = NULL,
  weights_validation = NULL,
  weights_x = NULL,
  weights_y = NULL,
  nbCores = 1,
  grid = 10,
  eps_default = 0.5,
  R2bound = NULL,
  unchanged = FALSE,
  ties = FALSE
)

Value

a list containing, in order: - S: the point estimation used the statistic for the test

- S_ci: the CI on the upper bound

- stat: the statistic of the test

- the critical value at level alpha

- the p_value of the test

- the fit with the OLS on this sample

- n the sample size

- epsilon, the choice of epsilon we made

- r2long the r2 on the long regression

-r2short the r2 on the short regression

Arguments

validation

dataset containing the joint distribution (Y,Xnc,Xc) where Y is the outcome, Xnc are the non commonly observed regressors, Xc are potential common regressors.

Ldata

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

Rdata

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

out_var

label of the outcome variable Y.

nc_var

label of the non commonly observed regressors Xnc.

c_var

label of the commonly observed regressors Xc.

alpha

the level of the confidence intervals. Default is 0.05.

constraint

a vector indicating the different constraints in a vector of the size of X_c indicating the type of constraints, if any on f(X_c) : "concave", "concave", "nondecreasing", "nonincreasing", "nondecreasing_convex", "nondecreasing_concave", "nonincreasing_convex", "nonincreasing_concave", or NULL for none. Default is NULL, no contraints at all.

nc_sign

if 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.

c_sign

if 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.

weights_validation

the sampling weights for the full dataset (Y, Xnc,Xc). Default is NULL.

weights_x

the sampling weights for the dataset (Xnc,Xc). Default is NULL.

weights_y

the sampling weights for the dataset (Y,Xc). Default is NULL.

nbCores

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

grid

the number of points for the grid search on epsilon. Default is 30. If NULL, then epsilon is taken fixed equal to eps_default.

eps_default

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

R2bound

the lower bound on the R2 of the long regression if any. Default is NULL.

unchanged

Boolean indicating if the categories based on Xc must be kept unchanged (TRUE). Otherwise (FALSE), a thresholding approach is taken imposing that each value appears more than 10 times in both datasets and 0.01 per cent is the pooled one. Default is FALSE.

ties

Boolean indicating if there are ties in the dataset. Default is FALSE.

Examples

### Simulating joint distribution according to this DGP
n=200
Xnc = rnorm(n,0,1.5)
epsilon = rnorm(n,0,1)

## true value
beta0 =1
Y = Xnc*beta0 + epsilon
out_var = "Y"
nc_var = "Xnc"

# create the datasets
validation<- as.data.frame(cbind(Y,Xnc))
colnames(validation) <- c(out_var,nc_var)


############# Estimation #############
test = point_ident_test (validation, Ldata=NULL,Rdata=NULL,out_var,nc_var)