The following features have been added to the QLIM procedure:
Bayesian Estimation Features. Most of the univariate models available in the QLIM procedure can be estimated in a Bayesian framework with the BAYES statement. The main features are as follows:
possibility of choosing the prior distributions through the PRIOR statement
several tools to control and optimize the initialization and the tuning phase
multithreaded Metropolis sampling
convergence diagnostic tools: Raftery-Lewis, Heidelberger-Welch, Geweke, effective sample size
prior and posterior predictive analysis
Heckman Selection Model – Two-Step Estimator. The QLIM procedure now supports Heckman’s two-step estimation method, as an alternative to maximum likelihood estimation of selection models. The standard errors of the second-step OLS estimates are corrected for consistency by default. However, if the uncorrected ones are requested for testing purposes, they are available with the UNCORRECTED option.
A new variable selection method. The greedy search method can be used with either forward selection or backward elimination. In each step, the AIC or BIC criterion is evaluated, and the selection continues until the selection criterion is met.
ODS Graphics plots for Bayesian and frequentist estimation methods. For the frequentist framework, the QLIM procedure can produce a graphical representation of the output that is produced with the OUTPUT statement. For the Bayesian approach, the QLIM procedure can produce the plots of the prior and the posterior predictive analysis.