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Extended regression models (ERM's)

ERMs is the name for regression models that can account for the following:

  1. Endogenous covariates
  2. Nonrandom treatment assignment
  3. Heckman-style endogenous sample selection

While Stata already had commands such as heckman and ivregress that can address these problems individually, ERMs can account for the problems in any combination. And ERMs don’t just address these problems in linear models.

There are four ERM commands:

  • eregress fits linear regression models for continuous outcomes.
  • eintreg fits interval regression, including tobit, for interval-measured and censored outcomes.
  • eprobit fits probit regression models for binary outcomes.
  • eoprobit fits ordered probit regression for ordinal outcomes.

You can now fit models that were previously unavailable, even if you need only one of the new features, such as:

  • interval regression with endogenous covariates
  • probit regression with a binary endogenous covariate
  • probit regression with endogenous ordinal treatment
  • ordered probit regression with endogenous treatment
  • linear regression with tobit endogenous sample selection
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