Stata 15 help for erm_intro

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:

------------------------------------------------------------------------- [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 -------------------------------------------------------------------------

If you are already familiar with ERMs, see the following help files for descriptions of the commands for fitting ERMs:

------------------------------------------------------------------------- [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 -------------------------------------------------------------------------

See the following help files for descriptions of the commands available after fitting ERMs:

------------------------------------------------------------------------- [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 -------------------------------------------------------------------------

The following manual entries demonstrate examples of how to fit models using eregress, eintreg, eprobit, and eoprobit:

------------------------------------------------------------------------- [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 -------------------------------------------------------------------------

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/.


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