Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down at the end of May, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Lloyd Dumont <lloyddumont@yahoo.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Endogenous Regressors Predicted by the Same IV |

Date |
Mon, 1 Mar 2010 07:48:03 -0800 (PST) |

Hello, Statalist. I’m pretty sure that what we’re trying to do is mathematically estimable under certain assumptions. So, we are trying to figure out the syntax for the estimating procedure. And, then we’d like to clarify the assumptions that have to hold for us to accept the estimates. We are ultimately trying to estimate a dep var we will call Y_dep. Y_dep is being predicted by X_1, X_2, …X_10. But, X_1, X_2, and X_3 are all endogenous. We believe they can all be predicted by the same instrumental variable, Z_1. And, furthermore, we are willing to accept that Z_1 has no direct effect on Y_dep beyond its effects through X_1, X_2, and X_3. Just to make this all a tad more complicated, Y_dep is actually binary, and, this is all being done with survey data. A simple approach using… svy linearized : ivprobit … seems to preclude our removing certain regressors from the first stage, which we think MAY be part of the required solution to the problem. If we are wrong about that, then there may be a way to use ivprobit? But, we're open to ANY suggestions. Thank you for your help. Lloyd. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

- Prev by Date:
**st: AW: AW: testing a model** - Next by Date:
**re: st: re: Solving the moving average in the error structure in a** - Previous by thread:
**Re: st: Repeated time values within panel in levpet STATA output** - Next by thread:
**re: st: Endogenous Regressors Predicted by the Same IV** - Index(es):