Allin I have tried what you suggested me and every change I imagined and I keep getting the same. This is what I have in the script: arima 0 1 1 ; 0 1 1 ; y --nc addobs 12 smpl --full fcast --out-of-sample matrix yhat = $fcast matrix se=$fcerr matrix ci = (yhat - 1.96*se) ~ (yhat + 1.96*se) series fc1=yhat[,1] series minor=ci[,1] series mayor=ci[,2] .and this is what I get For 95% confidence intervals, z(0.025) = 1.96 y prediction std. error 95% interval 1997:06 132791. 33883.8 66379.9 - 199202. 1997:07 178246. 34280.3 111058. - 245434. 1997:08 130851. 34672.3 62894.7 - 198808. 1997:09 119282. 35059.9 50565.9 - 187998. 1997:10 176718. 35443.3 107251. - 246186. 1997:11 166081. 35822.5 95869.8 - 236291. 1997:12 164873. 36197.8 93926.3 - 235819. 1998:01 148782. 36569.2 77108.0 - 220457. 1998:02 140043. 36936.9 67647.5 - 212437. 1998:03 153097. 37301.0 79989.0 - 226206. 1998:04 167374. 37661.5 93558.7 - 241189. 1998:05 151507. 38018.6 76991.9 - 226022. 1998:06 133511. 38372.4 58302.9 - 208720. Can you please tell me what am I doing wrong? Thanks On Tue, 4 Dec 2012, Miviam wrote: time. They will increase over time only if the forecast is dynamic. The most natural way to ensure that is to forecast out of sample. <hansl> open data9-7 # out-of-sample observations to reserve scalar os = 8 smpl ; -os arma 1 1 1 ; QNC fcast --out-of-sample matrix yhat = $fcast matrix se = $fcerr matrix ci = (yhat - 1.96*se) ~ (yhat + 1.96*se) matrix results = yhat ~ se ~ ci colnames(results, "yhat s.e. low high") print results </hansl> Allin Cottrell