The Durbin Watson statistic is designed to detect autocorrelation and is defined by D W frac sum t 2 T left(y t y t 1 ight) 2 sum t 1 T left(y t bar y ight) 2 a Derive the approximate relationship between (D W ) and the lag 1 autocorrelation coefficient (r 1 ) b Suppose that (r 1 0 4 ) What is the ...

a Deriving the approximate relationship between the DurbinWatson statistic DW and the lag 1 auto ...

The Durbin-Watson statistic is designed to detect autocorrelation and is defined by [D W=frac{sum_{t=2}^{T}left(y_{t}-y_{t-1} ight)^{2}}{sum_{t=1}^{T}left(y_{t}-bar{y} ight)^{2}} .]

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The Durbin-Watson statistic is designed to detect autocorrelation and is defined by

\[D W=\frac{\sum_{t=2}^{T}\left(y_{t}-y_{t-1}\right)^{2}}{\sum_{t=1}^{T}\left(y_{t}-\bar{y}\right)^{2}} .\]

a. Derive the approximate relationship between $D W$ and the lag 1 autocorrelation coefficient $r_{1}$.

b. Suppose that $r_{1}=0.4$. What is the approximate value of $D W$ ?

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Regression Modeling With Actuarial And Financial Applications

ISBN: 9780521135962

1st Edition

Authors: Edward W. Frees

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Question Posted: Apr 20, 2024 09:00 AM

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The Durbin-Watson statistic is designed to detect autocorrelation and is defined by [D W=frac{sum_{t=2}^{T}left(y_{t}-y_{t-1} ight)^{2}}{sum_{t=1}^{T}left(y_{t}-bar{y} ight)^{2}} .]

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Regression Modeling With Actuarial And Financial Applications

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