*! version 1.0.0 23may2021

Title

posis -- Partialling-out estimator based on iterative sure
independence screening.

Syntax

posis
depvar
varsofinterest
[if] [in]
,
controls(varlist)
model(model_spec)
[options]

varsofinterest are variables for which coefficients and their
standard errors are estimated.

* controls(varlist) specify the set of control variables

* model(model_spec) specify the
model

method(method_spec) specify the
variable selection technique

maxiter(#) specify the maximum number of
iterations

model_spec

linear linear regression

logit logit regression

poisson Poisson regression

method_spec

stepbic BIC-based stepwise

lasso , lasso_spec lasso

lasso_spec

cv cross-validation

plugin plug-in method

adaptive adaptive lasso

bic minimize BIC; the default

* controls() and model() are required.

For model_spec, only one of linear,
logit, or poisson is allowed.

For method_spec, only one of stepbic or
lasso is allowed.

For lasso_spec, only one of cv, plugin,
adaptive, or bic is allowed.

Description

posis fits a high-dimensional linear, logit or Poisson regression model
and reports standard errors, test statistics, and confidence intervals for
specified covariates of interest. The iterative sure-independence screening
partialing-out method developed in D. Drukker and D. Liu (2022a) and (2022b) is
used to estimate effects for these variables and to select from potential
control variables to be included in the model.

Options

controls(varlist)
specifies the set of control variables, which control for omitted variables.
Control variables are also known as confounding variables. posis uses the
lasso-based or BIC-stepwise-based iterative sure independence screening to
select the control variables for each of depvar and varsofinterest.
controls() is required.

model(model_spec)
specifies the model for the outcome variable depvar. model_spec can be
one of linear, logit, or poisson model. model() is
required.

method(method_spec)
specifies the covariate selection technique to be
used within sure independence screening. method_spec is one of
stepbic or lasso, lasso_spec, where stepbic refers to the
BIC-based forward stepwise methods and lasso refers to the Lasso; see
lasso.

lasso_spec specifies how to chose the tuning parameter in Lasso, and it can
be one of cv, plugin, adaptive, or bic. See
sel_method in lasso.

The default is using Lasso and chosing the tuning parameter by minimizing BIC,
which is equivalent to specifying method(lasso, bic).

always(varlist) specifies the variables that will always be included
in the model. The default is none.

maxiter(#) specifies the maximum number of iterations. The default is
5.

Examples

Setup

. webuse breathe

Partialing-out linear regression for outcome reaction time and inference
on classroom and home nitrogen oxide using BIC-lasso-based iterative sure
independence screening to select controls

. posis react no2_class no2_home,
controls(i.(meducation overweight msmoke sex) noise_school sev_home sev_school age)
model(linear)

As above but use BIC-stepwise-based iterative sure independence screening
to select controls

. posis react no2_class no2_home,
controls(i.(meducation overweight msmoke sex) noise_school sev_home sev_school age)
model(linear) method(stepbic)

Stored results

posis stores the following in e():

Scalars

e(N) number of observations

e(k_controls) number of controls

e(k_controls_sel) number of selected controls

e(k_varsofinterest) number of variables of interest

e(rank) rank of e(V)

Macros

e(cmd) posis

e(varsofinterest) variables of interest

e(depvar) dependent variable

e(controls_sel) selected control variables

e(controls) control variables

e(model) type of model

e(title) title in estimation output

e(vcetype) robust

e(vce) Robust

e(properties) b V

Matrices

e(b) coefficient vector

e(V) variance-covariance matrix of the estimators

Functions

e(sample) marks estimation sample

Reference

Drukker, D. M., and D. Liu. 2022a. Finite-sample results for lasso and stepwise
Neyman-orthogonal Poisson estimators. Econometric Reviews 41(9): 1047–1076.

Drukker, D. M., and D. Liu. 2022b. posis: Stata command for the
sure-independence-screening Neyman-orthogonal estimator.