Model estimates with balance repeated replication (BRR) based standard errors
svrmodel varlist [if exp] [in range] [, deff deft cmd(command name) level(#) or command_options ]
This command is for use with replication weights. You must set your data for replication based survey estimation with svrset or survwgt before using this command.
Typing svrmodel without arguments redisplays previous results.
Description
svrmodel estimates regression-type models for complex survey data. Standard errors are calculated using a series of user-supplied replication weights, by balanced repeated replication (BRR) or survey jackknife (JK1, JK2, or JKn). This is an alternate method to the Taylor series linearization methods used by Stata's svy commands. See survwgt for details on the creation of weights and estimation of variances with replication.
svrmodel will run ols regression, logit/probit, ordered logit/probit, multinomial logit, and poisson regression models. For other models, see svrest.
Options
deff and deft request that design effects deff and deft be displayed with the model estimates. See [R] svymean for details.
cmd() specifies the model estimation command. Valid options are regress logit probit logistic oprobit, ologit, mlogit and poisson. The default is ols regression.
or specifies that coefficients from a logit model should be displayed as odds-ratios. See logit.
Example command and output
. svrmodel income region sex asset
OLS estimates with replicate-based (jkn) standard errors
Analysis weight: wgt Number of obs = 1904 Replicate weights: jkn_1... Population size = 10738 Number of replicates: 30 Degrees of freedom = 20 F( 3, 18) = 86.40 Prob > F = 0.0000 R-squared = 0.0057
----------------------------------------------------------------------- ------- income | Coef. Std. Err. t P>|t| [95% Conf. In > terval] -------------+--------------------------------------------------------- ------- region | .6214526 .116294 5.34 0.000 .3788676 . > 8640376 sex | -1.121041 .2671536 -4.20 0.000 -1.678313 -. > 5637684 asset | .1274654 .0077771 16.39 0.000 .1112427 . > 1436881 _cons | 37.61598 .5194737 72.41 0.000 36.53238 3 > 8.69959 ----------------------------------------------------------------------- -------
Saved Results
svrmodel is an estimation command, so it saves model estimates and svr-based (c > o)variance matrix in e(b) and e(V) and creates e(sample) to reflect the estimation sample. > It also stores design effects in e(deff) and e(deft), and the simple-random-sampling-without-replacement (co) > variance matrix in e(V_srs).
svytest will estimate adjusted Wald linear hypothesis tests after BRR model estimation. (svrmodel specifies the estimation command as "svysvrmodel" in order to allow svytest to function.)
Scalars e(N_strata) and e(N_psu) are set in order to allow svytest to operate c > orrectly. N_strata is set to the degrees of freedom for the model (by default the number of replic > ates), and N_psu is set to twice the degrees of freedom.
predict after an estimation should work as documented for the relevant command. > Warning: this has not been tested extensively.
Methods and formulae
See survwgt.
Acknowledgements
I would like to thank Bobby Gutierrez at StataCorp for advice on implementation of BRR.
Author
Nick Winter Cornell University nw53@cornell.edu