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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,
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