QLIM Procedure
PROC QLIM Statement
PROC QLIM <options>;
You can specify the following options in the PROC QLIM statement.
Data Set Options
Output Data Set Options
- OUTEST=SAS-data-set
writes the parameter estimates to the specified SAS-data-set.
- COVOUT
writes the covariance matrix for the parameter estimates to the OUTEST= data set. This option is valid only if the OUTEST= option is specified.
- CORROUT
writes the correlation matrix for the parameter estimates to the OUTEST= data set. This option is valid only if the OUTEST= option is specified.
Printing Options
- NOPRINT
suppresses the normal printed output but does not suppress error listings. If you specify the NOPRINT option, then any other print option is turned off.
- PRINTALL
turns on all the printing-control options. The options set by PRINTALL are COVB and CORRB.
- CORRB
- COVB
- ITPRINT
prints the initial parameter estimates, convergence criteria, and all constraints of the optimization. At each iteration, objective function value, step size, maximum gradient, and slope of search direction are printed as well.
Model Estimation Options
- COVEST=OP | HESSIAN | QML
-
specifies the method for calculating the covariance matrix of parameter estimates. You can specify the following covariance-options:
- OP
calculates the covariance from the outer product matrix.
- HESSIAN
calculates the covariance from the inverse Hessian matrix.
- QML
calculates the covariance from the outer product and Hessian matrices (the quasi-maximum likelihood estimates).
By default, COVEST=HESSIAN.
- HECKIT <(heckit-options)>
-
uses Heckman’s two-step estimation method to estimate the selection model. You must specify exactly two MODEL statements when you use the HECKIT option. One of the models must be a binary probit model; therefore, you must specify the DISCRETE option in the MODEL or ENDOGENOUS statement. You base the selection on the binary probit model for the second model; therefore, you must specify the SELECT option for this model.
You can specify one or both of the following heckit-options:
- SECONDSTAGE=OLS | ML
-
specifies the estimation method of the second stage of Heckman’s two-step method. You can specify the following values:
- OLS
requests the ordinary least squares method for the second stage. If you specify SECONDSTAGE=OLS, then the model of interest—that is, the model that uses the SELECT option—must be linear and contain a continuous dependent variable. Therefore, you cannot specify the DISCRETE, FRONTIER, CENSORED, or TRUNCATED option along with the SELECT option for the model of interest. When you specify SECONDSTAGE=OLS, you cannot test or restrict the parameters of the model of interest. However, you can test or restrict the parameters of the selection model—that is, the model that defines the selection rule.
- ML
requests that PROC QLIM use the maximum likelihood method in the second stage, as it does in the first stage. When you specify SECONDSTAGE=ML, the model of interest can be either linear with a continuous dependent variable or nonlinear with a censored or truncated continuous dependent variable. However, nonlinear models, including discrete and frontier models, are not supported. Therefore, you can specify the CENSORED or TRUNCATED option along with the SELECT option for the model of interest, but you cannot specify the DISCRETE or FRONTIER option with the SELECT option. Moreover, you can also use the TEST or RESTRICT statement to test or restrict the parameters of the model of interest.
By default, SECONDSTAGE=OLS.
- UNCORRECTED
-
requests the conventional OLS standard errors when the second-stage estimation method is the ordinary least squares method. If you do not specify the UNCORRECTED option, PROC QLIM reports the corrected OLS standard errors. For more information about the corrected standard errors, see the section Heckman’s Two-Step Selection Method.
If you specify both the UNCORRECTED and SECONDSTAGE=ML options, PROC QLIM ignores the UNCORRECTED option, because the UNCORRECTED option is related to the OLS standard errors.
- NDRAW=value
- SEED=value
specifies a seed for pseudorandom number generation in Monte Carlo integration.
Optimization Process Control Options
PROC QLIM uses the nonlinear optimization (NLO) subsystem to perform nonlinear optimization tasks. You can use any of the NLO options in the NLOPTIONS statement. For more information, see Chapter 6, Nonlinear Optimization Methods.
- METHOD=value
-
specifies the optimization method. If this option is specified, it overwrites the TECH= option in the NLOPTIONS statement. You can specify the following values:
- CONGRA
performs a conjugate-gradient optimization.
- DBLDOG
performs a version of double-dogleg optimization.
- NEWRAP
performs a Newton-Raphson optimization, combining a line-search algorithm with ridging.
- NMSIMP
performs a Nelder-Mead simplex optimization.
- NONE
specifies that no optimization be performed beyond using the ordinary least squares method to compute the parameter estimates.
- NRRIDG
performs a Newton-Raphson optimization with ridging.
- QUANEW
performs a quasi-Newton optimization.
- TRUREG
performs a trust region optimization.
By default, METHOD=QUANEW.
Plotting Options
Global Plot Options
You can specify the following global-plot-options:
- ONLY
displays only the requested plot.
- PRIOR
displays the prior predictive graph that is associated with the requested posterior predictive plot BAYESPRED. This option is available only for Bayesian analysis.
-
UNPACKPANEL
UNPACK specifies that all paneled plots be unpacked, meaning that each plot in a panel is displayed separately.
Plot Requests
You can specify the following plot-requests:
- ALL
specifies all types of available plots.
- AUTOCORR<(LAGS=n)>
displays the autocorrelation function plots for the parameters. This plot-request is available only for Bayesian analysis. The optional LAGS= suboption specifies the number (up to lag n) of autocorrelations to be plotted in the AUTOCORR plot. If this suboption is not specified, autocorrelations are plotted up to lag 50.
- BAYESDIAG
displays the TRACE, AUTOCORR, and DENSITY plots. This plot-request is available only for Bayesian analysis.
- BAYESPRED
displays the predictive analysis. The predictive analysis takes into account the variability of the error term, whereas the PREDICTED plot-request does not. The BAYESPRED plot-request is available only for Bayesian analysis.
- BAYESSUM
displays the posterior distribution, the prior distribution, and the maximum likelihood estimates. This plot-request is available only for Bayesian analysis.
- CONDITIONAL
displays the conditional expected values for continuous endogenous variables. Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This plot-request is not available for Bayesian analysis.
- DENSITY<(FRINGE)>
displays the kernel density plots for the parameters. This plot-request is available only for Bayesian analysis. If you specify the FRINGE suboption, a fringe plot is created on the X axis of the kernel density plot. This plot-request is available only for Bayesian analysis.
- ERRSTD
displays the error standard deviation versus observed regressors when you also specify a HETERO statement. This plot-request is not available for Bayesian analysis.
- EXPECTED
displays the expected values for continuous endogenous variables. Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This plot-request is not available for Bayesian analysis.
- MARGINAL
displays the marginal effects. Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This plot-request is not available for Bayesian analysis.
- MILLS
displays the inverse Mills ratio. Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This plot-request is not available for Bayesian analysis.
- NONE
suppresses all diagnostic plots.
- PREDICTED
displays the model predicted values. Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This plot-request is not available for Bayesian analysis.
- PROB
displays the predicted response probability. Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This plot-request is not available for Bayesian analysis.
- PROBALL
displays the predicted probabilities for each level of the response. Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This plot-request is not available for Bayesian analysis.
- PROFLIK
displays the profiled log likelihood. Each profiled graph is obtained by setting all the parameters to their maximum likelihood estimate except for the profiling parameter. The profiling parameter takes values on a predefined grid that is determined by the maximum likelihood estimate of the corresponding standard deviation. When a restricted optimization is requested, the profiled log likelihood plots depict the behavior of the profiled log likelihood around the restricted MLE without imposing the actual restrictions.
- RESIDUAL
displays the residuals versus observed regressors. This plot-request is not available for Bayesian analysis.
- TE1
displays the technical efficiency for the stochastic frontier model as suggested by Battese and Coelli (1988). Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This plot-request is not available for Bayesian analysis.
- TE2
displays the technical efficiency for the stochastic frontier model as suggested by Jondrow et al. (1982). Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This plot-request is not available for Bayesian analysis.
- TRACE<(SMOOTH)>
displays the trace plots for the parameters. This plot-request is available only for Bayesian analysis. The SMOOTH suboption displays a fitted penalized B-spline curve for each TRACE plot.
- XBETA
displays the structural part on the right-hand side of the model. Each contributing regressor is set equal to its mean, except for the parameter that is reported on the X axis. This is not available for Bayesian analysis.