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help for ^reoprob^                               (STB-59: sg158; STB-61: sg158.1)
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Random-effects ordered probit
-----------------------------

	^reoprob^ depvar varlist [^if^ exp] [^in^ range] ^,^ 
			[ ^i(^varname^)^ ^q^uadrat^(^#^)^ ^l^evel^(^#^)^ maximize_options ]

This command shares the features of all estimation commands; see help @est@.

To reset problem-size limits, see help @matsize@.


Description
-----------

^reoprob^ estimates a random-effects ordered probit model for panel datasets
using maximum likelihood estimation.  The likelihood for each unit is
approximated by Gauss-Hermite quadrature.


Options
-------

^i(^varname^)^ specifies the variable corresponding to an independent unit
    (e.g., a subject id).  ^i(^varname^)^ is not optional.

^quadrat(^#^)^ specifies the number of points to use for Gaussian-Hermite
    quadrature. It is optional, and the default is 12. Increasing this value
    improves accuracy, but also increases computation time.  Computation time
    is roughly proportional to its value.

^level(^#^)^ specifies the confidence level, in percent, for confidence
    intervals.  The default is ^level(95)^ or as set by ^set level^.

maximize_options controls the maximization process and the display of
    information; see [R] maximize. ^nolog^ suppresses the display of the 
    likelihood iterations.  Use the ^trace^ option to view parameter
    convergence.  The ^ltol(^#^)^ and ^tol(^#^)^ option can be used to loosen
    the convergence criterion (respectively 1e-7 and 1e-6 by default) during
    specification searches. ^iter(^#^)^ specifies the maximum number of
    iterations.


Examples
--------

   . ^reoprob y x, i(id)^
   . ^reoprob y x^
   . ^reoprob y x, i(id) quad(24) nolog^
   . ^reoprob y x, i(id) trace^
   . ^reoprob^


Method
------

^reoprob^ uses the d1 method (analytic first derviatives) of Stata's ^ml^
commands.  See Butler and Moffitt (1982) for details about using Gauss-Hermite
quadrature to approximate such integrals. Also see Green (2000) for
information on how to estimate a basic ordered probit model.


Author
------

	Guillaume R. Frechette
	Ohio State University
	Department of Economics
	410 Arps Hall
	1945 North High Street
	Columbus, OH 43210-1172
	Tel: (614) 688-4140
	Fax: (614) 292-4192
	e-mail: frechette.6@@osu.edu
	http://www.econ.ohio-state.edu/frechette/


Reference
---------

Butler, J.S. and  R. Moffitt.  1982.  A computationally efficient 
     quadrature procedure for the one-factor multinomial probit model.  
     Econometrica 50: 761-764.

Green, W. H. 2000. Econometric Analysis. Prentice Hall, New Jersey. 
     pp. 875-878.

Also see
--------

 Manual:  ^[R] xt, [R] xtprobit, [R] maximize, [R] oprobit^
On-line:  help for @xt@, @xtreg@