*! version 1.1.0 1aug2022

Title

regsensitivity -- Regression sensitivity analysis

Syntax

regsensitivity subcommand ... [, options]

subcommand

bounds coefficient bounds

breakdown breakdown analysis

plot plot results

regsensitivity can be abbreviated to regsen.

Description

regsensitivity analyzes the sensitivity of regression coefficient estimates
to the presence of omitted variables. By default, relaxations of the no omitted
variables assumption are indexed by sensitivity parameters as defined in
Diegert, Masten, and Poirier (2022). The package also implements the sensitivity analysis
in Oster (2019) and Masten and Poirier (2022), which use a different set of sensitivity parameters.

regsensitivity bounds calculates bounds on the regression coefficient under
a range of alternative assumptions on the omitted variables.

regression breakdown calculates the maximum value of a sensitivity parameter
under which a given hypothesis holds for all values of the regression coefficients
in the identified set.

regsensitivity plot is a post-estimation command that can be run after
regsensitivity bounds or regsensitivity breakdown to visualize the results.

Further Information

This package implements the sensitivity analysis described in Diegert, Masten, and Poirier (2022),
Oster (2019), and Masten and Poirier (2022).

This vignette provides a tutorial for use of this package walking through the empirical application in
Diegert, Masten, and Poirier (2022)
using data from Bazzi, Fiszbein, and Gebresilasse (2020). If you are new to this package, this vignette is the best place to start.

References

Bazzi, Fiszbein, and Gebresilasse (2020) Frontier Culture: The Roots and Persistence of "Rugged Individualism" in the United States, Econometrica

Diegert, Masten, and Poirier (2022) Assessing Omitted Variable Bias when the Controls are Endogenous, arXiv preprint

Masten, and Poirier (2022) The Effect of Omitted Variables on the Sign of Regression Coefficients, arXiv preprint

Oster (2019) Unobservable Selection and Coefficient Stability: Theory and Evidence, Journal of Business & Economic Statistics