The QLIM Procedure

Specifies the input data set

QLIM

DATA=

Writes parameter estimates to an output data set

QLIM

OUTEST=

Writes predictions to an output data set

OUTPUT

OUT=

Specifies BY-group processing

BY

Specifies classification variables

CLASS

Specifies a frequency variable

FREQ

Specifies a weight variable

WEIGHT

NONORMALIZE

Requests all printing options

QLIM

PRINTALL

Prints correlation matrix of the estimates

QLIM

CORRB

Prints covariance matrix of the estimates

QLIM

COVB

Prints a summary iteration listing

QLIM

ITPRINT

Suppresses the normal printed output

QLIM

NOPRINT

Displays plots

QLIM

PLOTS=

Specifies the optimization method

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

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 pseudo-random number generation

QLIM

SEED=

Specifies the number of draws for Monte Carlo integration

QLIM

NDRAW=

Specifies the method to calculate parameter covariance

QLIM

COVEST=

Requests estimation by Heckman’s two-step method

QLIM

HECKIT

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

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=

Displays convergence diagnostics

BAYES

DIAGNOSTICS=

Displays summary statistics of the posterior samples

BAYES

STATISTICS=

Specifies a SAS data set for the posterior samples

BAYES

OUTPOST=

Specifies a SAS data set for the prior samples

BAYES

OUTPRIOR=

Specifies normal prior distribution

PRIOR

NORMAL (MEAN=, VAR=)

Specifies gamma prior distribution

PRIOR

GAMMA (SHAPE=, SCALE=)

Specifies inverse gamma prior distribution

PRIOR

IGAMMA (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=)

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()

Requests the variable addition test for endogeneity

ENDOGENOUS

ENDOTEST()

Requests the overidentification test

ENDOGENOUS

OVERID()

Specifies the function for heteroscedasticity models

HETERO

LINK=

Squares the function for heteroscedasticity models

HETERO

SQUARE

Specifies no constant for heteroscedasticity models

HETERO

NOCONST

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

QLIM

COVOUT

Includes correlations in the OUTEST= data set

QLIM

CORROUT

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