**erm introduction **-- Introduction to erm

__Description__

ERM stands for extended regression model, a term we at Stata created.
Although the term is new, the method is not. ERMs are regression models
with continuous outcomes (including censored and tobit outcomes), binary
outcomes, and ordered outcomes that are fit with maximum likelihood.
These models can account for endogenous covariates, sample selection, and
nonrandom treatment assignment. ERMs provide a unifying framework for
handling these complications individually or in combination.

__Resources__

If you are new to ERMs, see the introductions in the following manual
entries:

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**[ERM] intro** Introduction
**[ERM] intro 1** An introduction to the ERM commands
**[ERM] intro 2** The models that ERMs fit
**[ERM] intro 3** Endogenous covariates features
**[ERM] intro 4** Endogenous sample-selection features
**[ERM] intro 5** Treatment assignment features
**[ERM] intro 6** Model interpretation
**[ERM] intro 7** A Rosetta stone for extended regression
commands
**[ERM] intro 8** Conceptual introduction via worked
example
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If you are already familiar with ERMs, see the following help files for
descriptions of the commands for fitting ERMs:

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**[ERM] eintreg** Extended interval regression
**[ERM] eoprobit** Extended ordered probit regression
**[ERM] eprobit** Extended probit regression
**[ERM] eregress** Extended linear regression
**[ERM] erm options** Extended regression model options
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See the following help files for descriptions of the commands available
after fitting ERMs:

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**[ERM] eintreg postestimation** Postestimation tools for eitnreg
**[ERM] eintreg predict** predict after eitnreg
**[ERM] eoprobit postestimation** Postestimation tools for eoprobit
**[ERM] eoprobit predict** predict after eoprobit
**[ERM] eprobit postestimation** Postestimation tools for eprobit
**[ERM] eprobit predict** predict after eprobit
**[ERM] eregress postestimation** Postestimation tools for eregress
**[ERM] eregress predict** predict after eregress
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The following manual entries demonstrate examples of how to fit models
using **eregress**, **eintreg**, **eprobit**, and **eoprobit**:

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**[ERM] example 1a** Linear regression with continuous
endogenous covariate
**[ERM] example 1b** Interval regression with continuous
endogenous covariate
**[ERM] example 1c** Interval regression with endogenous
covariate and sample selection
**[ERM] example 2a** Linear regression with binary endogenous
covariate
**[ERM] example 2b** Linear regression with exogenous
treatment
**[ERM] example 2c** Linear regression with endogenous
treatment
**[ERM] example 3a** Probit regression with continuous
endogenous covariate
**[ERM] example 3b** Probit regression with endogenous
covariate and treatment
**[ERM] example 4a** Probit regression with endogenous sample
selection
**[ERM] example 4b** Probit regression with endogenous
treatment and sample selection
**[ERM] example 5** Probit regression with endogenous ordinal
treatment
**[ERM] example 6a** Ordered probit regression with endogenous
treatment
**[ERM] example 6b** Ordered probit regression with endogenous
treatment and sample selection
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__Reference__

Gould, W. W. 2018. Ermistatas and Stata's new ERMs commands. The Stata
Blog: Not Elsewhere Classified.
https://blog.stata.com/2018/03/27/ermistatas-and-statas-new-erms-comm
> ands/.