CMP: Stata module to implement conditional (recursive) mixed process estimator. cmp estimates multi-equation, mixed process models, potentially with hierarchical random effects. ”Mixed process” means that different equations can have different kinds of dependent variables. The choices are: continuous (like OLS), tobit (left-, right-, or bi-censored), probit, ordered probit or fractional probit. ”Conditional” means that the model can vary by observation. An equation can be dropped for observations for which it is not relevant--if, say, a worker retraining program is not offered in a city then the determinants of uptake cannot be modeled there. Or the type of dependent variable can vary by observation. A dependent variable in one equation can appear on the right side of another equation. Such dependencies must have a recursive structure if the dependencies are on censored variables as observed, meaning that they split the equations into stages. If the dependencies are on (latent) linear dependent variables, they can be recursive or simultaneous in structure. So cmp can fit many SUR, simultaneous equation, and IV models. cmp’s modeling framework therefore embraces those of the official Stata commands probit, ivprobit, treatreg, biprobit, tetrachoric, oprobit, mprobit, asmprobit, asroprobit, tobit, ivtobit, cnreg, intreg, truncreg, heckman, heckprob, xtreg, xtprobit, xttobit, xtintreg, in principle even regress, sureg, and reg3. It goes beyond them in offering far more flexibility in model construction. The routine runs under Stata 10 or later, faster under Stata 11.2 or later.
References in zbMATH (referenced in 1 article )
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- Gouret, Fabian; Hollard, Guillaume; Rossignol, Stéphane: An empirical analysis of valence in electoral competition (2011)