TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 68

SAS/Ets User's Guide 68. 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 | 662 F Chapter 12 The ENTROPY Procedure Experimental wage-dependent firm relocation oil market dynamics Getting Started ENTROPY Procedure This section introduces the ENTROPY procedure and shows how to use PROC ENTROPY for several kinds of statistical analyses. Simple Regression Analysis The ENTROPY procedure is similar in syntax to the other regression procedures in SAS. To demonstrate the similarity suppose the endogenous dependent variable is y and x1 and x2 are two exogenous independent variables of interest. To estimate the parameters in this single equation model using PROC ENTROPY use the following SAS statements proc entropy model y x1 x2 run Test Scores Data Set Consider the following test score data compiled by Coleman et al. 1966 title Test Scores compiled by Coleman et al. 1966 data coleman input test_score teach_sal prcnt_prof socio_stat teach_score mom_ed label test_score Average sixth grade test scores in observed district label teach_sal Average teacher salaries per student 1000s of dollars label prcnt_prof Percent of students fathers with professional employment label socio_stat Composite measure of socio-economic status in the district label teach_score Average verbal score for teachers label mom_ed Average level of education years of the students mothers datalines . more lines . This data set contains outliers and the condition number of the matrix of regressors X is large which indicates collinearity amoung the regressors. Since the maximum entropy estimates are both robust with respect to the outliers and also less sensitive to a high condition number of the X matrix maximum entropy estimation is a good choice for this problem. Simple Regression Analysis F 663 To fit a simple linear model to this data by using PROC ENTROPY use the following statements proc entropy data coleman model test_score teach_sal prcnt_prof socio_stat teach_score mom_ed run This requests the estimation of a linear model for TEST_SCORE with the .

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