TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 119

SAS/Ets User's Guide 119. 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 | 1172 F Chapter 18 The MODEL Procedure The preceding DATA step generates the data set to request a five-days-ahead forecast. The following statements estimate and forecast the three forward-rate models of the following form. ratet ratet-i p ratet-i v v a ratet-1 e e N 0 1 title Daily Multivariate Geometric Brownian Motion Model of D-Mark USDollar Forward Rates proc model data xfrate parms df 15 Give initial value to df demusd1m lag demusd1m mu1m lag demusd1m var_demusd1m sigma1m 2 lag demusd1m 2 demusd3m lag demusd3m mu3m lag demusd3m var_demusd3m sigma3m 2 lag demusd3m 2 demusd6m lag demusd6m mu6m lag demusd6m var_demusd6m sigma6m 2 lag demusd6m 2 Specify the error distribution errormodel demusd1m demusd3m demusd6m t var_demusd1m var_demusd3m var_demusd6m df output normalized S matrix fit demusd1m demusd3m demusd6m outsn s run forecast five days in advance solve demusd1m demusd3m demusd6m data five sdata s random 1500 out monte id date run select out the last date --- data monte set monte if date 10dec95 d then output run title Distribution of demusd1m Five Days Ahead proc univariate data monte noprint var demusd1m histogram demusd1m normal noprint color red kernel noprint color blue cfill ligr run The Monte Carlo simulation specified in the preceding example draws from a multivariate t distribution with constant degrees of freedom and forecasted variance and it computes future states of DEMUSD1M DEMUSD3M and DEMUSD6M. The OUTSN option in the FIT statement is used to specify the data set for the normalized Z matrix. That is the Z matrix is created by crossing the Alternate Distribution Simulation F 1173 normally distributed residuals. The normally distributed residuals are created from the t distributed residuals by using the normal inverse CDF and the t CDF. This matrix is a correlation matrix. The distribution of DEMUSD1M on the fifth day is shown in the Figure . The two curves overlaid on the graph are a kernel density estimation and a normal distribution .

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