TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 144

SAS/Ets User's Guide 144. 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 | 1422 F Chapter 21 The QLIM Procedure Example Tobit Analysis. 1469 Example Bivariate Probit Analysis. 1471 Example Sample Selection Model . 1472 Example Sample Selection Model with Truncation and Censoring . . 1473 Example Types of Tobit Models . 1476 Example Stochastic Frontier Models. 1482 References . 1486 Overview QLIM Procedure The QLIM qualitative and limited dependent variable model procedure analyzes univariate and multivariate limited dependent variable models in which dependent variables take discrete values or dependent variables are observed only in a limited range of values. These models include logit probit tobit selection and multivariate models. The multivariate model can contain discrete choice and limited endogenous variables in addition to continuous endogenous variables. The QLIM procedure supports the following models linear regression model with heteroscedasticity Box-Cox regression with heteroscedasticity probit with heteroscedasticity logit with heteroscedasticity tobit censored and truncated with heteroscedasticity bivariate probit bivariate tobit sample selection and switching regression models multivariate limited dependent variables stochastic frontier production and cost models In the linear regression models with heteroscedasticity the assumption that error variance is constant across observations is relaxed. The QLIM procedure allows for a number of different linear and nonlinear variance specifications. Another way to make the linear model more appropriate to fit the data and reduce skewness is to apply Box-Cox transformation. If the nature of data is such that the dependent variable is discrete and it takes only two possible values OLS estimates are inconsistent. The QLIM procedure offers probit and logit models to overcome these estimation problems. Assumptions about the error variance can also be relaxed in order to estimate probit or logit with heteroscedasticity. Getting Started QLIM Procedure F 1423

• Tài chính doanh nghiệp
• Hướng dẫn sử dụng SAS ETS
• các mô hình phân tích tài chính
• báo cáo tài chính
• phân tích đầu tư
• phân tích tính khả dụng các khoản đầu tư