Homework2SolS11

Homework2SolS11 - Assignment 2 sample solution a Multiple...

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Assignment 2 sample solution a) Multiple linear regression model The ACF plot suggests that the residuals of the multiple linear regression model are correlated, up to and including lag 16. This suggests that an auto- regressive (AR) model should be used to eliminate the correlation between the residuals. a8 <- arima(CPIRECNS, order=c(8,0,0), xreg=data.frame(JTSJOL, GAS), method="ML") Call: arima(x = CPIRECNS, order = c(8, 0, 0), xreg = data.frame(JTSJOL, GAS), method = "ML")
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Assignment 2 sample solution Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 0.5086 0.3255 0.3548 -0.0378 0.0409 0.0676 -0.0355 -0.2768 s.e. 0.0862 0.0998 0.1025 0.1087 0.1080 0.1035 0.0993 0.0877 intercept JTSJOL GAS 6900.518 -28.6014 16.0394 s.e. 5112.792 47.0768 51.2934 sigma^2 estimated as 24894: log likelihood = -799.39, aic = 1622.78
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Assignment 2 sample solution b) Verifying model residuals are white noise and normally distributed The residuals appear to be random and uncorrelated with a mean of approximately 0. >runs.test(a8$residuals, plot.it=TRUE)
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This note was uploaded on 02/23/2012 for the course STAT 443 taught by Professor Yuliagel during the Spring '09 term at Waterloo.

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Homework2SolS11 - Assignment 2 sample solution a Multiple...

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