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SAS/ETS 9.22 User's Guide 149

SAS/Ets 9.22 User's Guide 149. Provides detailed reference material for using SAS/ETS software and guides you through the analysis and forecasting of features such as univariate and multivariate time series, cross-sectional time series, seasonal adjustments, multiequational nonlinear models, discrete choice models, limited dependent variable models, portfolio analysis, and generation of financial reports, with introductory and advanced examples for each procedure. You can also find complete information about two easy-to-use point-and-click applications: the Time Series Forecasting System, for automatic and interactive time series modeling and forecasting, and the Investment Analysis System, for time-value of money analysis of a variety of investments | 1472 F Chapter 21 The QLIM Procedure Output 21.3.1 Bivariate Probit Analysis Results Estimating a Tobit model The QLIM Procedure Model Fit Summary Number of Endogenous Variables 2 Endogenous Variable y1 y2 Number of Observations 500 Log Likelihood -134.90796 Maximum Absolute Gradient 3.23363E-7 Number of Iterations 17 Optimization Method Quasi-Newton AIC 283.81592 Schwarz Criterion 313.31817 Parameter Estimates Standard Approx Parameter DF Estimate Error t Value Pr t yl.Intercept 1 1.003639 0.153678 6.53 .0001 yl.xl 1 2.244374 0.256062 8.76 .0001 y1.x2 1 3.273441 0.341581 9.58 .0001 y2.Intercept 1 3.621164 0.457173 7.92 .0001 y2.x1 1 4.551525 0.576547 7.89 .0001 y2.x2 1 -2.442769 0.332295 -7.35 .0001 _Rho 1 0.144097 0.336459 0.43 0.6685 Example 21.4 Sample Selection Model This example illustrates the use of PROC QLIM for sample selection models. The data set is the same one from Mroz 1987 . The goal is to estimate a wage offer function for married women accounting for potential selection bias. Of the 753 women the wage is observed for 428 working women. The labor force participation equation estimated in the introductory example is used for selection. The wage equation uses log wage lwage as the dependent variable. The explanatory variables in the wage equation are the woman s years of schooling educ wife s labor experience exper and square of experience expersq . The program is as follows Sample Selection proc qlim data mroz model inlf nwifeinc educ exper expersq age kidslt6 kidsge6 discrete model lwage educ exper expersq select inlf 1 run The output of the QLIM procedure is shown in Output 21.4.1. Example 21.5 Sample Selection Model with Truncation and Censoring F 1473 Output 21.4.1 Sample Selection Binary Data The QLIM Procedure Model Fit Summary Number of Endogenous Variables 2 Endogenous Variable inlf lwage Number of Observations 753 Log Likelihood -832 88509 Maximum Absolute Gradient 0 00502 Number of Iterations 78 Optimization Method Quasi-Newton AIC 1694 .