Typical ACF for ARMA(1,1) has initial dropoff followed by exponential decay try ARMA(1,1) on – span 12 differences. ESTIMATE P=1 Q=1 ml; FORECAST lead=44 id=date interval = month out=outARIMA; run; Standard Approx Parameter Estimate Error t Value Pr > |t| Lag MU 0.0058962 0.0029586 1.99 0.0463 0 MA1,1 0.11374 2.87 0.0041 1 AR1,1 0.79326 0.07210 11.00 <.0001 1 Autocorrelation Check of Residuals To Chi- Pr > Lag Square DF ----------------Autocorrelations---------------- 0.7699 6 1.81 4 0.022 -0.055 -0.000 0.011 -0.028 -0.071 0.1367 12 14.87 10 0.109 0.044 0.003 0.060 0.211 -0.064 0.4248 18 16.41 16 0.010 -0.012 0.036 0.014 -0.066 0.038 0.7468 24 17.30 22 -0.056 0.020 0.004 0.018 -0.009 -0.017 0.1961 30 34.15 28 -0.131 -0.052 -0.144 0.134 0.026 -0.133 0.0969 36 45.08 34 -0.102 0.051 0.135 -0.107 0.014 -0.074 PROC SGPLOT data=outARIMA; ;
Typical ACF for ARMA(1,1) has initial dropoff followed by exponential decay – try ARMA(1,1) on span 12 differences. ESTIMATE P=1 Q=1 ml; FORECAST lead=44 id=date interval = month out=outARIMA; run; Standard Approx Parameter Estimate Error t Value Pr > |t| Lag MU 0.0058962 0.0029586 1.99 0.0463 0 MA1,1 0.11374 2.87 0.0041 1 AR1,1 0.79326 0.07210 11.00 <.0001 1 Autocorrelation Check of Residuals To Chi- Pr > Lag Square DF ----------------Autocorrelations---------------- 6 1.81 4 0.7699 0.022 -0.055 -0.000 0.011 -0.028 -0.071 12 14.87 10 0.1367 0.109 0.044 0.003 0.060 0.211 -0.064 18 16.41 16 0.4248 0.010 -0.012 0.036 0.014 -0.066 0.038 24 17.30 22 0.7468 -0.056 0.020 0.004 0.018 -0.009 -0.017 30 34.15 28 0.1961 -0.131 -0.052 -0.144 0.134 0.026 -0.133 36 45.08 34 0.0969 -0.102 0.051 0.135 -0.107 0.014 -0.074 PROC SGPLOT data=outARIMA; ;
Dickey_Advenures%20in%20Forecasting%20Short%20Course.docx