The QLIM Procedure

Functional Summary

Table 27.1 summarizes the statements and options used with the QLIM procedure.

Table 27.1: 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: November 05, 2018