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SAS/Ets 9.22 User's Guide 143. 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 | 1412 F Chapter 20 The PDLREG Procedure data a input ce ca @@ qtr mod _n_-1 4 1 q1 qtr 1 q2 qtr 2 q3 qtr 3 datalines . more lines . proc pdlreg data a model ce q1 q2 q3 ca 5 2 dwprob run The printed output produced by the PDLREG procedure is shown in Output 20.1.1. The small Durbin-Watson test indicates autoregressive errors. Output 20.1.1 Printed Output Produced by PROC PDLREG National Industrial Conference Board Data Quarterly Series - 1952Q1 to 1967Q4 The PDLREG Procedure Dependent Variable ce Ordinary Least Squares Estimates SSE 1205186.4 DFE 48 MSE 25108 Root MSE 158.45520 SBC 733.84921 AIC 719.797878 MAE 107.777378 AICC 722.180856 MAPE 3.71653891 HQC 725.231641 Durbin- -Watson 0.6157 Regress R-Square 0.9834 Total R-Square 0.9834 Parameter Estimates Standard Approx Variable DF Estimate Error t Value Pr t Intercept 1 210.0109 73.2524 2.87 0.0061 q1 1 -10.5515 61.0634 -0.17 0.8635 q2 1 -20.9887 59.9386 -0.35 0.7277 q3 1 -30.4337 59.9004 -0.51 0.6137 ca 0 1 0.3760 0.007318 51.38 .0001 ca 1 1 0.1297 0.0251 5.16 .0001 ca 2 1 0.0247 0.0593 0.42 0.6794 Example 20.1 Industrial Conference Board Data F 1413 Output 20.1.1 continued Estimate of Lag Distribution Variable Estimate Standard Approx Error t Value Pr t ca 0 0.089467 0.0360 2.49 0.0165 ca 1 0.104317 0.0109 9.56 .0001 ca 2 0.127237 0.0255 5.00 .0001 ca 3 0.158230 0.0254 6.24 .0001 ca 4 0.197294 0.0112 17.69 .0001 ca 5 0.244429 0.0370 6.60 .0001 Variable ca 0 ca 1 ca 2 ca 3 ca 4 Estimate of Lag Distribution 0 0.2444 ca 5 The following statements use the REG procedure to fit the same polynomial distributed lag model. A DATA step computes lagged values of the regressor X and RESTRICT statements are used to impose the polynomial lag distribution. Refer to Judge et al. 1985 pp. 357-359 for the restricted least squares estimation of the Almon distributed lag model. data b set a ca_1 lag ca ca_2 lag2 ca ca_3 lag3 ca ca_4 lag4 ca ca_5 lag5 ca run proc reg data b model ce q1 q2 q3 ca ca_1 ca_2 ca_3 ca_4 ca_5 restrict - ca
