QLIM Procedure
Functional Summary
Table 1 summarizes the statements and options used with the QLIM procedure.
Table 1: PROC QLIM Functional Summary
| Description | Statement | Option |
|---|---|---|
| Data Set Options | ||
| Specifies the input data set | PROC QLIM | DATA= |
| Writes parameter estimates to an output data set | PROC QLIM | OUTEST= |
| Writes predictions to an output data set | OUTPUT | OUT= |
| Declaring the Role of Variables | ||
| Specifies BY-group processing | BY | |
| Specifies classification variables | CLASS | |
| Specifies a frequency variable | FREQ | |
| Specifies a weight variable | WEIGHT | NONORMALIZE |
| Printing Control Options | ||
| Requests all printing options | PROC QLIM | PRINTALL |
| Prints correlation matrix of the estimates | PROC QLIM | CORRB |
| Prints covariance matrix of the estimates | PROC QLIM | COVB |
| Prints a summary iteration listing | PROC QLIM | ITPRINT |
| Suppresses the normal printed output | PROC QLIM | NOPRINT |
| Plotting Options | ||
| Displays plots | PROC QLIM | PLOTS= |
| Options to Control the Optimization Process | ||
| Specifies the optimization method | PROC QLIM | METHOD= |
| Specifies the optimization options | NLOPTIONS | See Chapter 6, Nonlinear Optimization Methods. |
| Sets initial values for parameters | INIT | |
| Specifies upper and lower bounds for the parameter estimates | BOUNDS | |
| Specifies linear restrictions on the parameter estimates | RESTRICT | |
| Model Estimation Options | ||
| Specifies options specific to Box-Cox transformation | MODEL | BOXCOX() |
| Suppresses the intercept parameter | MODEL | NOINT |
| Specifies variable selection | MODEL | SELECTVAR=( ) |
| Specifies the type of random number generators | MODEL | RANDNUM= |
| Specifies that initial values are generated using random numbers | MODEL | RANDOMINIT |
| Specifies a seed for pseudorandom number generation | PROC QLIM | SEED= |
| Specifies the number of draws for Monte Carlo integration | PROC QLIM | NDRAW= |
| Specifies the method to calculate parameter covariance | PROC QLIM | COVEST= |
| Requests estimation by Heckman’s two-step method | PROC QLIM | HECKIT |
| Options for the Estimation of Random-Parameters Models | ||
| Specifies the ID variable for the parameter heterogeneity | RANDOM | SUBJECT= |
| Requests the MC simulation method of integration | RANDOM | METHOD=SIMULATION() |
| Requests the Halton sequence method of integration | RANDOM | METHOD=HALTON() |
| Requests the Gauss-Hermite quadrature method of integration | RANDOM | METHOD=HERMITE() |
| Requests that random parameters be uncorrelated | RANDOM | NOCORRELATION |
| Bayesian MCMC Options | ||
| Controls the aggregation of multiple posterior chains | BAYES | AGGREGATION= |
| Automates the initialization of the MCMC algorithm | BAYES | AUTOMCMC() |
| Specifies the initial values of the MCMC | INIT | |
| Evaluates the marginal likelihood | BAYES | MARGINLIKE |
| Specifies the maximum number of tuning phases | BAYES | MAXTUNE= |
| Specifies the minimum number of tuning phases | BAYES | MINTUNE= |
| Specifies the number of burn-in iterations | BAYES | NBI= |
| Specifies the number of iterations during the sampling phase | BAYES | NMC= |
| Specifies the number of samples for the prior predictive analysis | BAYES | NMCPRIOR= |
| Specifies the number of threads to use during the sampling phase | BAYES | NTRDS= |
| Specifies the number of iterations during the tuning phase | BAYES | NTU= |
| Controls options for constructing the initial proposal covariance matrix | BAYES | PROPCOV= |
| Specifies the sampling scheme | BAYES | SAMPLING= |
| Specifies the random number generator seed | BAYES | SEED= |
| Prints the time required for the MCMC sampling | BAYES | SIMTIME |
| Controls the thinning of the Markov chain | BAYES | THIN= |
| Bayesian Summary Statistics and Convergence Diagnostics | ||
| Displays convergence diagnostics | BAYES | DIAGNOSTICS= |
| Displays summary statistics of the posterior samples | BAYES | STATISTICS= |
| Bayesian Prior and Posterior Samples | ||
| Specifies a SAS data set for the posterior samples | BAYES | OUTPOST= |
| Specifies a SAS data set for the prior samples | BAYES | OUTPRIOR= |
| Bayesian Analysis | ||
| Specifies normal prior distribution | PRIOR | NORMAL(MEAN=, VAR=) |
| Specifies gamma prior distribution | PRIOR | GAMMA(SHAPE=, SCALE=) |
| Specifies square root gamma prior distribution | PRIOR | SQGAMMA(SHAPE=, SCALE=) |
| Specifies inverse gamma prior distribution | PRIOR | IGAMMA(SHAPE=, SCALE=) |
| Specifies square root inverse gamma prior distribution | PRIOR | SQIGAMMA(SHAPE=, SCALE=) |
| Specifies uniform prior distribution | PRIOR | UNIFORM(MIN=, MAX=) |
| Specifies beta prior distribution | PRIOR |
BETA(SHAPE1=, SHAPE2=, MIN=, MAX=) |
| Specifies t prior distribution | PRIOR | T(LOCATION=, DF=) |
| Endogenous Variable Options | ||
| Specifies discrete variable | ENDOGENOUS | DISCRETE() |
| Specifies censored variable | ENDOGENOUS | CENSORED() |
| Specifies truncated variable | ENDOGENOUS | TRUNCATED() |
| Specifies variable selection condition | ENDOGENOUS | SELECT() |
| Specifies stochastic frontier variable | ENDOGENOUS | FRONTIER() |
| Endogeneity and Overidentification Test Options | ||
| Requests the variable addition test for endogeneity | ENDOGENOUS | ENDOTEST() |
| Requests the overidentification test | ENDOGENOUS | OVERID() |
| Heteroscedasticity Model Options | ||
| Specifies the function for heteroscedasticity models | HETERO | LINK= |
| Squares the function for heteroscedasticity models | HETERO | SQUARE |
| Specifies no constant for heteroscedasticity models | HETERO | NOCONST |
| Output Control Options | ||
| Outputs predicted values | OUTPUT | PREDICTED |
| Outputs structured part | OUTPUT | XBETA |
| Outputs residuals | OUTPUT | RESIDUAL |
| Outputs error standard deviation | OUTPUT | ERRSTD |
| Outputs marginal effects | OUTPUT | MARGINAL |
| Outputs probability for the current response | OUTPUT | PROB |
| Outputs probability for all responses | OUTPUT | PROBALL |
| Outputs expected value | OUTPUT | EXPECTED |
| Outputs conditional expected value | OUTPUT | CONDITIONAL |
| Outputs inverse Mills ratio | OUTPUT | MILLS |
| Outputs technical efficiency measures | OUTPUT | TE1 |
| OUTPUT | TE2 | |
| Includes covariances in the OUTEST= data set | PROC QLIM | COVOUT |
| Includes correlations in the OUTEST= data set | PROC QLIM | CORROUT |
| Test Request Options | ||
| Requests Wald, Lagrange multiplier, and likelihood ratio tests | TEST | ALL |
| Requests the Wald test | TEST | WALD |
| Requests the Lagrange multiplier test | TEST | LM |
| Requests the likelihood ratio test | TEST | LR |
Last updated: June 19, 2025