help ueve
-------------------------------------------------------------------------------

Title

    [R] ueve --   Unbiased errors-in-variables estimator (UEVE),
                    Errors-in-variables estimator (EVE) and Efficient Wald
                    estimator (EWALD) regressions on grouped data


Syntax

        ueve depvar [indepvars] [if] [in] [weight] , group(groupvar)
               [estimator level(#)]


    estimator               description
    -------------------------------------------------------------------------
    ueve                    Unbiased errors-in-variables estimator (UEVE),
                              the default
    eve                     Errors-in-variables estimator (EVE)
    ewald                   Efficient Wald estimator (EWALD)
    -------------------------------------------------------------------------

    xi is allowed; see prefix.
    pweights, iweights, aweights and fweights are allowed; see weight.


Description

    ueve fits a linear regression of depvar on indepvars, using one of the
    three estimators for grouped data: Devereux (2007) errors-in-variables
    estimator that is approximately unbiased (UEVE); Deaton (1985)
    errors-in-variables estimator (EVE) that was shown to be equivalent to
    Jackknife Instrumental Variable Estimator in Devereux (2007); and
    Efficient Wald estimator (EWALD) (Angrist 1991).  Input data should be in
    individual-level (not grouped) format, as the program uses them to
    compute estimates of variance of sampling errors necessary to correct the
    bias in the grouping estimator. Since the variance of the sampling errors
    is estimated using group-level sampling variances with (groupsize-1) in
    denominator, groups cannot have less than two observations. If any group
    has one observation in it, the program automatically drops such groups
    from estimation, and a warning message is displayed. Variance-covariance
    matrix of the estimator is calculated using formula following Deaton
    (1985).


Options

        +-------+
    ----+ Model +------------------------------------------------------------

    group(groupvar) specifies the name of the grouping variable. The data
        must be divided into a set of mutually exclusive and exhaustive
        groups indexed by groupvar.

        +-----------+
    ----+ Reporting +--------------------------------------------------------

    level(#); see [R] estimation options.


Examples

    Setup
        . sysuse nlsw88
        . egen grp=group(south smsa c_city industry)

    Fit UEVE regression in which observations are grouped by groups grp,
    which are allocated into mutually exclusive and exhaustive supersets
    indexed by categorical variables south, smsa, c_city, industry.
        . ueve wage race married grade tenure, group(grp)

    Fit EWALD regression.
        . ueve wage race married grade tenure, group(grp) ewald


Saved results

    ueve saves the following in e():

    Scalars        
      e(N)                number of observations
      e(G)                number of groups
      e(r2)               R-squared (defined using data grouped into means)

    Macros         
      e(cmd)              ueve
      e(depvar)           name of dependent variable
      e(indepvars)        indepvars
      e(properties)       b V

    Matrices       
      e(b)                coefficient vector
      e(V)                variance-covariance matrix of the estimators

    Functions      
      e(sample)           marks estimation sample


References

Angrist, Joshua D., 1991. "Grouped-data estimation and testing in simple
    labor-supply models," Journal of Econometrics, Elsevier, vol. 47(2-3),
    pages 243-266, February.

Deaton, A. 1985. "Panel data from a time series of cross-sections," Journal of
    Econometrics, 30, 109- 126.

Devereux, Paul J., 2007. "Improved Errors-in-Variables Estimators for Grouped
    Data," Journal of Business & Economic Statistics, American Statistical
    Association, vol. 25, pages 278-287, July.


Author

        Aliaksandr Amialchuk, University of Toledo, USA
        aamialc@utnet.utoledo.edu


Also see

    Online:  [R] eivreg;
             [R] ivregress;
             [R] regress