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From |
"David Roodman ([email protected])" <[email protected]> |

To |
<[email protected]> |

Subject |
st: new estimation command for mixed, multi-eq probit, tobit, continuous |

Date |
Thu, 18 Oct 2007 07:56:28 -0400 |

I'm happy to announce a new estimation command. It is called "cmp," for conditional mixed process. It fits multi-equation models that can mix probit, tobit, and "continuous" (unbounded/OLS-like) dependent variables. In effect, it subsumes the official commands biprobit, ivprobit, and ivtobit, and the user-written mvprobit, bitobit, triprobit, and mvtobit, and does much more. The "conditional" means that which equations are included in the model can vary by observation. The program requires Stata 10. So for example, you could a regress a censored variable on two endogenous variables, one binary and one continuous and uncensored, each instrumented with other variables. To install, type "ssc install cmp". This is brand new, so I welcome comments. A slightly more detailed explanation, from the help file: cmp estimates multi-equation, conditional recursive mixed process models. "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), and probit. A dependent variable in one equation can appear on the right side of another equation. However, cmp can only fit "recursive" models with clearly defined stages, not ones with simultaneous causation. A and B can be determinants of C and C a determinant of D--but D cannot be a determinant of A, B, or C. And the "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, though the utility of this freedom is less obvious cmp's modeling framework therefore embraces those of the official Stata commands probit, ivprobit, biprobit, tobit, and ivtobit, in principle even regress, sureg, ivreg, as well as the user-written triprobit, mvprobit, bitobit, and and mvtobit. It goes beyond them in offering far more flexibility in model construction. To take one arbitrary example, one could regress a continuous variable on two endogenous variables, one binary and the other sometimes left-censored, instrumenting each with additional variables. And it allows the models to vary by observation. In some cases, the gain is consistent estimation where it was difficult before. In other cases, the gain is just in efficiency. For example if y is continuous, x is a sometimes-left-censored determinant of y, and z is an instrument, then the effect of x on y can be consistently estimated with 2SLS (Kelejian 1971). However, a cmp estimate that uses the information that x is censored will be more efficient, based as it is on a more accurate model. David Roodman Research Fellow Center for Global Development [email protected] http://www.cgdev.org +1 (202) 416-0723 1776 Massachusetts Ave. NW Washington, DC 20036 * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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