# hwk6 - IEOR E4709 Data Analysis for Financial Engineers...

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IEOR E4709 Data Analysis for Financial Engineers Solution 6 1. First we store the f rst 76 observations. #First, we will create a time series object. logJJhwk - logJJ[1:76,] Next we f tthea ir l inemode l . #fit the airline model with s=4 JJtemphwk - arima.mle(logJJhwk, model=list( list(order=c(0, 1, 1), period=4), list(order=c(0, 1, 1)) ) ) #Model Output JJtemphwk Call: arima.mle(x = logJJhwk, model = list(list(order = c(0, 1, 1), period = 4), list(order = c(0, 1, 1)))) Method: Maximum Likelihood Model #1: Model: 011 Period : 4 Coefficients: MA : 0.3492 Model #2: Coefficients: MA : 0.65589 Variance-Covariance Matrix: ma(1) ma(4) ma(1) 0.008601888 -0.002764281 ma(4) -0.002764281 0.013255396 Optimizer has converged Convergence Type: relative function convergence AIC: -132.56821 Therefore, the f tted model is (1 )(1 ) =(1  )(1 Θ ) with =0 65589 Θ 3492 1

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Next we shall make a prediction. jjFcsthwk.dates - timeSeq(from="1/1/1979", to="10/1/1980", by="quarters", format="%Y:%Q") jjForecasthwk - arima.forecast (logJJhwk, n=8, model=JJtemphwk\$model, future.positions=jjFcsthwk.dates) jjForecasthwk \$mean: Positions 1 1979:I 2.585637 1979:II 2.627074 1979:III 2.611076 1979:IV 2.364515 1980:I 2.729193 1980:II 2.770630 1980:III 2.754632 1980:IV 2.508071 \$std.err: Positions 1 1979:I 0.09169830 1979:II 0.09697545 1979:III 0.10197989 1979:IV 0.10674998 1980:I 0.14042359 1980:II 0.14977353 1980:III 0.15857313 1980:IV 0.16690945 Finally we make a prediction plot. plot(exp(logJJ[positions(logJJ) = timeDate("1/1/1977"),]), exp(jjForecasthwk\$mean), exp(jjForecasthwk\$mean+1.96*jjForecasthwk\$std.err), exp(jjForecasthwk\$mean - 1.96*jjForecasthwk\$std.err), plot.args=list(lty=c(1, 4, 3, 3)) )
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## This note was uploaded on 10/17/2010 for the course IEOR 4709 taught by Professor Stevenkou during the Spring '10 term at Columbia.

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hwk6 - IEOR E4709 Data Analysis for Financial Engineers...

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