模型的比较和平均-

1、模型的比较和平均>da=read.table('q-gdpc96.txt',header=T>head(daYear Mon Day gdp21947411769.48631947711768.03251948111823.36561948411856.930>gdpts=ts(da$gdp,frequency=4,start=c(1947,1,1>plot(gdpts,lty=1,col='purple' >lngdpts=ts(log(da$gdp,frequency=4,start=c(1947,1,1 >plot(ln

2、gdpts,lty=1,col='red' >gdpratets=ts(diff(da$gdp/da$gdp,frequency=4,start=c(194 7,1,1 >gdp=log(da$gdp>dgdp=diff(gdp>m1=ar(dgdp,method='mle'>m1$order13>m2=arima(dgdp,order=c(3,0,0>m2Call:arima(x=dgdp,order=c(3,0,0Coefficients:ar1ar2ar3interceptsigma2estimated as8.3

3、23e-05:log likelihood=829.23, aic=-1648.45拟合的AR(3模型为:-0.007 (1-0.3462B-0.1299B2+0.1225B3(xt9=at>m3=arima(dgdp,order=c(3,0,0,seasonal=list(order=c(1,0, 1,period=4>m3Call:arima(x=dgdp,order=c(3,0,0,seasonal=list(order=c(1,0,1,period=4Coefficients:ar1ar2ar3sar1sma1interceptsigma2estimated as8.24e

4、-05:log likelihood=830.47,aic=-1646.93拟合的季节模型为:-0.0079=(1-0.587B4 (1-0.3305B-0.1521B2+0.1103B3(1-0.497B4(xtat>source('backtest.R'>mm2=backtest(m2,dgdp,215,11"RMSE of out-of-sample forecasts"#预测误差平方根10.0061531021"Mean absolute error of out-of-sample forecasts"#平方绝对预测误差10.004430387>mm3=backtest(m3,dgdp,215,11"RMSE of out-of-sample forecasts"10.0063220091"Mean absolute error of out-of-sample forecasts"10.004553896>acf(dgdp,lag=16 >pacf(dgdp,lag=16 >tsdiag(m2,gof=36#