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help for svyselmlog
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Selectivy-adjusted equation based in the multinomial logit for survey data

        svyselmlog depvar [varlist], [if exp] [in range] , select([depvar_s]
                varlist_s ) [method(sel_method) showmlogit
                mloptions(mlogit_options) gen(newvarname) bootstrap(# of
                replications) force quiet]

        
        
  where sel_meth is one of the following selectivity correction methods:

        
    lee Lee (1983), Econometrica

        
    dmf Dubin and McFadden (1984), Econometrica

        
    dhl (# [all]) Dahl (2002), Econometrica. Where # is the order of the
        polinomial used in the conditional probability of observing the
        selected outcome. The option all includes the conditional
        probabilites of both, the selected and non-selected outcomes in
        polinomial form of degree #.

        
    dmf2 Bourguignon, Fournier and Gurgand (2004), CREST Working Paper.


        svyselmlog requires that the survey design variables be identified usin
> g svyset, see help svyset


Description

        
    svyselmlog estimates selectivity-adjusted regression models using a
    multinomial logit for complex survey data.

        
    svyselmlog estimates all the parameters of the following model:

                Main equation: y_j = Xb_j + u_{1j}
                
                Selection equation: y_j is observed if V_j > max{V_i} for all i
>  different from j
        
        where: V_j = Zd_j + u_{2j} 
                 Corr(u_{1j},u_{2j}) = rho   
        
        
    Selectivity bias is corrected using the conditional probabilities based
    on the multinomial logit. In the syntax for svyselmlog, depvar and
    varlist are the dependent variable (y_j) and regressors (X) for the main
    equations, respectively; depvar_m and varlist_m are the discrete choice
    variable and regressors in the selection equation.  The outcome variable
    (y_j) is observed for only one value of the discrete variable (depvar_m),
    therefore depvar should have missing values for all other values of
    depvar_m.

        
    Since svyselmlog is based on selmlog (see help selmlog; available from 
    M. Gurgand), it allows several forms of selection correction methods as
    reviewed in Bourguignon, Fournier and Gurgand (2004).  Given J different
    choices in the multinomial model, svyselmlog estimates a series of
    variables labelled _mk , k = 1...J containing the conditional
    probablities following the chosen parameterization method. In a
    seconds-step the _mk variables are used as selectivity-controls in the
    main equation.

        
    The reported standard errors do not account for the two-step nature of
    the procedure (i.e. they are not consistent), however their empirical
    distribution can be obtained using bootstrap methods. Bootstapping within
    a complex survey design has to account for stratification and clustering;
    the bootstrap option used in svyselmlog accounts for this using 
    svybsamp2, therefore svybsamp2 must be intalled (see:  ssc install
    svybsamp2).


Options

        
    method (sel_method) specifies the selection-adjustment method that should
        be used to generate the _mk controls.  If no method is selected dmf2
        is used as the default option.

        
    showmlogit reports the estimated selection equation (multinomial logit)

        
    mloptions(mlogit_options) parse the specified options as described in 
        svymlogit to the multinomial logit estimation.

        
    gen (newvarname) generates a series of new variables equal to _mk

        
    bootstrap (# of replications) reports the bootstrapped standard errors
        out of # replications. svybsamp2 must be installed.

    force forces the estimation even in the presence of strata with a singel
        PSU (singleton). This option temporary drops the singleton(s) from
        the estimation, reporting how many singleton strata and observations
        were eliminated from the estimation.  {p 4 8 2} quiet suppresses the
        message indicating the current re-sampling # within the bootstrap
        estimation.  Examples {p 4 4 2} svyselmlog wage x1 x2,
        select(occupation x1 x2 z1 z2) meth(dhl(3 all)) {p 4 4 2} svyselmlog
        wage x1 x2, select(occupation x1 x2 z1 z2) meth(dmf) boot(100) quiet
        References {p 4 8 2} Bourguignon, F., Fournier, M. and Gurgand, M.
        (2004) `Selection bias correction based on the multinomial logit
        model: Monte-Carlo comparisons', Mimeo DELTA, Paris {p 4 8 2} Dahl,
        G.B. (2002) `Mobility and the return to education: testing a Roy
        model with multiple markets', Econometrica, vol. 70, 6.  {p 4 8 2}
        Dubin, J.A. and McFadden, D. (1984) `An econometric analysis of
        residential electric appliance holdings and consumption',
        Econometrica, vol. 52, 2.  {p 4 8 2}Lee, L.F. (1983) `Generalized
        econometric models with selectivity', Econometrica, vol. 51, pp.
        507-512.  Author Rafael E. De Hoyos, Faculty of Economics, University
        of Cambridge. red29@cam.ac.uk Also see Manual:  [U] 23 Estimation and
        post-estimation commands [SVY] svy estimators Online:  help for 
        selmlog (if installed), svybsamp2 (if installed), svyset, svyheckman,