SAS/Ets User's Guide 46. 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 | 442 F Chapter 8 The AUTOREG Procedure Output continued Variable DF Parameter Estimates Standard Approx Pr t Estimate Error t Value ARCH0 1 .0001 ARCH1 1 ARCH2 1 Output Conditional Variance for IBM Stock Prices Example Estimation of GARCH-Type Models This example extends Example to include more volatility models and to perform model selection and diagnostics. Following is the data of daily IBM stock prices for the long period from 1962 to 2009. Example Estimation of GARCH-Type Models F 443 data ibm_long infile datalines format date MMDDYY10. input date MMDDYY10. price_ibm r 100 dif log price_ibm datalines 01 02 1962 01 03 1962 01 04 1962 01 05 1962 . more lines . 08 12 2009 The time series of IBM returns is depicted graphically in Output . Output IBM Stock Returns Daily The following statements perform estimation of different kinds of GARCH-type models. First ODS listing output that contains fit summary tables for each single model is captured by using an ODS OUTPUT statement with the appropriate ODS table name assigned to a new SAS data set. Along 444 F Chapter 8 The AUTOREG Procedure with these new data sets another one that contains parameter estimates is created by using the OUTEST option in AUTOREG statement. Capturing ODS tables into SAS data sets ods output fitsum_ar_1 ods output fitsum_arch_2 ods output fitsum_garch_1_1 ods output fitsum_st_garch_1_1 ods output fitsum_ar_1_garch_1_1 ods output fitsum_igarch_1_1 ods output fitsum_garchm_1_1 ods output
