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Using the ADL(1,1)regression Yt = ?0 + ?1Yt-1 γ1\gamma _ { 1 }

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Using the ADL(1,1)regression Yt = ?0 + ?1Yt-1 + γ1\gamma _ { 1 } Xt-1 + ut, the ARCH model for the regression error assumes that ut is normally distributed with mean zero and variance σt2\sigma _ { t } ^ { 2 } , where
A) σt2=α0+α1ut12+α2ut22++αputp2\sigma _ { t } ^ { 2 } = \alpha _ { 0 } + \alpha _ { 1 } u _ { t - 1 } ^ { 2 } + \alpha _ { 2 } u _ { t - 2 } ^ { 2 } + \ldots + \alpha _ { p } u _ { t - p } ^ { 2 } .
B) σt2=ut12++utp2+φ1σt12++φqσtq2\sigma _ { t } ^ { 2 } = u _ { t - 1 } ^ { 2 } + \ldots + u _ { t - p } ^ { 2 } + \varphi _ { 1 } \sigma _ { t - 1 } ^ { 2 } + \ldots + \varphi _ { q } \sigma _ { t - q } ^ { 2 } .
C) σt2=φ1σt12++φqσtq2\sigma _ { t } ^ { 2 } = \varphi 1 \sigma _ { t - 1 } ^ { 2 } + \ldots + \varphi _ { q } \sigma _ { t - q } ^ { 2 } .
D) σt2=α0+α1ut12++αputp2+φ1σt12++φqσtq2\sigma _ { t } ^ { 2 } = \alpha _ { 0 } + \alpha _ { 1 } u _ { t - 1 } ^ { 2 } + \ldots + \alpha _ { p } u _ { t - p } ^ { 2 } + \varphi _ { 1 } \sigma _ { t - 1 } ^ { 2 } + \ldots + \varphi _ { q } \sigma _ { t - q } ^ { 2 } .


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