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help for ^ivprob^, ^divprob^, and ^ivtobit^                    
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Instrumental variables probit and tobit using Amemiya Generalized Least Squares
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        ^ivprob^, ^divprob^, ^ivtobit^   

^ivprob^ and ^ivtobit^ are used for estimating probit models where one or more 
of the independent variables is endogenous.  Neither weights nor the @robust@
option are implemented.

^divprob^ produces marginal effects for ^ivprob^. 

Description
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These programs implement Amemiya Generalized Least Squares (AGLS) estimators for probit
and tobit with endogenous regressors.  Newey (1987, eq. 5.6) provides the formulas 
used.  The endogenous regressors are treated as linear functions of the instruments
and the other exogenous variables.  

For a good summary of how AGLS works, see Maddala (1983) pp. 247-252.

Syntax
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^ivprob^ depvar  [^if^ exp] [^in^ range], endog(varlist1) iv(varlist2) exog(varlist3)
^divprob^ depvar  [^if^ exp] [^in^ range], endog(varlist1) iv(varlist2) exog(varlist3) [CLAssic]
^ivtobit^ depvar  [^if^ exp] [^in^ range], endog(varlist1) iv(varlist2) exog(varlist3)
	[ll(#) ul(#)]

where:

   depvar is the dependent variable,
   varlist1 are the endogenous variables,
   varlist2 are the instruments for varlist1,
   varlist3 are the other exogenous variables, and
   ll and ul are the censoring points for the tobit (see @tobit@) with defaults of
      zero (for ll) and 9999999 (for ul).
   
For a description of the ^classic^ option with ^divprob^, see @dprobit@.

References
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Maddala, G. (1983).  Limited-Dependent and Qualitative Variables in Econometrics.
	Cambridge University Press.

Newey, W. (1987).  "Simultaneous estimation of limited dependent variable models with 
	endogenous explanatory variables."  Journal of Econometrics, 36: 231-250.

        
Acknowledgements
----------------

These programs were inspired by Jonah Gelbach's -probitiv- and -tobitiv- (available 
at http://glue.umd.edu/~gelbach/ado/).  -ivprob- and -ivtobit- provide correct
standard errors.  They should also be more efficient than Gelbach's implementation.

^divprob^ draws heavily on Stata's ^dprobit^.

Author
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        Joe Harkness, Johns Hopkins University, USA
        joe.harkness@@jhu.edu


Also see
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@ivreg@ 
@treatreg@ (stb 55)
