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According to Hamilton (1994), page 1-5:

suppose a process where: $y_{t} = b \times y_{t-1} + w_{t}$

Where $y_{t-1}$ is the realisation in the previous period and $w_{t}$ is some random innovation.

The long run effect therefore is the effect on $y_{t+1}$ from a permanent increase in $w$. Hence the long-run effect is ${1}/{(1-b)}$ for this special case (or the expected value of the function).

However, as far as I know the term is far more common for Vector Error Correction models. Hope this helps a bit.

According to Hamilton (1994), page 1-5:

suppose a process where: $y_{t} = b \times y_{t-1} + w_{t}$

Where $y_{t-1}$ is the realisation in the previous period and $w_{t}$ is some random innovation.

The long run effect therefore is the effect on $y_{t+1}$ from a permanent increase in $w$. Hence the long-run effect is ${1}/{(1-b)}$ for this special case (or the expected value of the function). For the short run effect I cannot find a proper source right now but I remember it being the coefficient ($b$ in this case).

However, as far as I know the term is far more common for Vector Error Correction models. Hope this helps a bit.

According to Hamilton (1994), page 1-5:

suppose a process where: y(t) = b x y(t-1) + w(t)

Where y(t-1) is the realisation in the previous period and w(t) is some random innovation.

The long run effect therefore is the effect on y(t+1) from a permanent increase in w. Hence the long-run effect is 1/(1-b) for this special case.

However, as far as I know the term is far more common for VEC models. Hope this helps a bit.

According to Hamilton (1994), page 1-5:

suppose a process where: $y_{t} = b \times y_{t-1} + w_{t}$

Where $y_{t-1}$ is the realisation in the previous period and $w_{t}$ is some random innovation.

The long run effect therefore is the effect on $y_{t+1}$ from a permanent increase in $w$. Hence the long-run effect is ${1}/{(1-b)}$ for this special case (or the expected value of the function).

However, as far as I know the term is far more common for Vector Error Correction models. Hope this helps a bit.

According to Hamilton (1994), page 5 formula, the effect on y(t+1) from a permanent increase in w. Therefore the long-run effect is 1/(1-theta). However, as far as I know the term is far more common for VEC models. Hope this helps a bit.

According to Hamilton (1994), page 1-5:

suppose a process where: y(t) = b x y(t-1) + w(t)

Where y(t-1) is the realisation in the previous period and w(t) is some random innovation.

The long run effect therefore is the effect on y(t+1) from a permanent increase in w. Hence the long-run effect is 1/(1-b) for this special case. 

However, as far as I know the term is far more common for VEC models. Hope this helps a bit.