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In this post - Rob Hyndman explains that:

Even with that correction, the two models are not quite equivalent. In the Eviews code, the differencing is done before estimation, whereas in the R code the differencing is implicit in the model. In estimating the model in R, a state space representation is used and the non-stationary components are given a diffuse prior, rather than simply differenced away. (See help(arima) in R.) This will lead to different parameter estimates.

But isn't the whole point of the "I" in ARIMA, is that we difference the data to get a stationary signal, to which we can then apply AR(p) and MA(q) terms?

How can implicit differencing be achieved?

What is a diffuse prior and how does it solve the problem of non-stationary components in an ARMA/ARIMA model?

More specifically: I understand that a diffuse prior is an uninformative prior, but what does that mean in the specific context of an ARIMA or an ARMA model?

In this post - Rob Hyndman explains that:

Even with that correction, the two models are not quite equivalent. In the Eviews code, the differencing is done before estimation, whereas in the R code the differencing is implicit in the model. In estimating the model in R, a state space representation is used and the non-stationary components are given a diffuse prior, rather than simply differenced away. (See help(arima) in R.) This will lead to different parameter estimates.

But isn't the whole point of the "I" in ARIMA, is that we difference the data to get a stationary signal, to which we can then apply AR(p) and MA(q) terms?

How can implicit differencing be achieved?

What is a diffuse prior and how does it solve the problem of non-stationary components in an ARMA/ARIMA model?

More specifically: I understand that a diffuse prior is an uninformative prior, but what does that mean in the specific context of an ARIMA or ARMA model?

In this post - Rob Hyndman explains that:

Even with that correction, the two models are not quite equivalent. In the Eviews code, the differencing is done before estimation, whereas in the R code the differencing is implicit in the model. In estimating the model in R, a state space representation is used and the non-stationary components are given a diffuse prior, rather than simply differenced away. (See help(arima) in R.) This will lead to different parameter estimates.

But isn't the whole point of the "I" in ARIMA, is that we difference the data to get a stationary signal, to which we can then apply AR(p) and MA(q) terms?

How can implicit differencing be achieved?

What is a diffuse prior and how does it solve the problem of non-stationary components in an ARMA/ARIMA model?

More specifically: I understand that a diffuse prior is an uninformative prior, but what does that mean in the specific context of an ARIMA or an ARMA model?

5 edited body
source | link

In this post - Rob Hyndman explains that:

Even with that correction, the two models are not quite equivalent. In the Eviews code, the differencing is done before estimation, whereas in the R code the differencing is implicit in the model. In estimating the model in R, a state space representation is used and the non-stationary components are given a diffuse prior, rather than simply differenced away. (See help(arima) in R.) This will lead to different parameter estimates.

But isn't the whole point of the "I" in ARIMA, is that we difference the modeldata to get a stationary signal, to which we can then apply AR(p) and MA(q) terms?

How can implicit differencing be achieved?

What is a diffuse prior and how does it solve the problem of non-stationary components in an ARMA/ARIMA model?

More specifically: I understand that a diffuse prior is an uninformative prior, but what does that mean in the specific context of an ARIMA or ARMA model?

In this post - Rob Hyndman explains that:

Even with that correction, the two models are not quite equivalent. In the Eviews code, the differencing is done before estimation, whereas in the R code the differencing is implicit in the model. In estimating the model in R, a state space representation is used and the non-stationary components are given a diffuse prior, rather than simply differenced away. (See help(arima) in R.) This will lead to different parameter estimates.

But isn't the whole point of the "I" in ARIMA, is that we difference the model to get a stationary signal to which we can then apply AR(p) and MA(q) terms?

How can implicit differencing be achieved?

What is a diffuse prior and how does it solve the problem of non-stationary components in an ARMA/ARIMA model?

More specifically: I understand that a diffuse prior is an uninformative prior, but what does that mean in the specific context of an ARIMA or ARMA model?

In this post - Rob Hyndman explains that:

Even with that correction, the two models are not quite equivalent. In the Eviews code, the differencing is done before estimation, whereas in the R code the differencing is implicit in the model. In estimating the model in R, a state space representation is used and the non-stationary components are given a diffuse prior, rather than simply differenced away. (See help(arima) in R.) This will lead to different parameter estimates.

But isn't the whole point of the "I" in ARIMA, is that we difference the data to get a stationary signal, to which we can then apply AR(p) and MA(q) terms?

How can implicit differencing be achieved?

What is a diffuse prior and how does it solve the problem of non-stationary components in an ARMA/ARIMA model?

More specifically: I understand that a diffuse prior is an uninformative prior, but what does that mean in the specific context of an ARIMA or ARMA model?

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How can differencing in an ARIMA model be implicit instead of explicit?

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