Calcite | Level 5

## Moving Average Errors vs. Error Correction Term (Serial Correlation)

I wanted to understand the difference between error correction term (Nlag in Proc Autoreg) and Moving Average Errors in Proc Arima.

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SAS Employee

## Re: Moving Average Errors vs. Error Correction Term (Serial Correlation)

FYI, you can find some detailed concept descriptions in http://www2.sas.com/proceedings/sugi28/252-28.pdf and the relationship between stationary AR model and MA model in https://www.otexts.org/fpp/8/4.

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Obsidian | Level 7

## Re: Moving Average Errors vs. Error Correction Term (Serial Correlation)

AR model: An autoregression mode is a regression of the variable against itself (past values of the forecast variable). An autoregressive model of order p, AR(p) can be written as yt=c+ϕ1yt1+ϕ2yt2++ϕpytp+et, where c is a constant and et is white noise.

MA model: In contrast to the AR model, a moving average model uses past forecast errors in a regression-like model. A moving average model of order q, MA(q) can be written as yt=c+et+θ1et1+θ2et2++θqetq, where et is white noise.

In both cases, the error term is white noise. And from the formula above, we can clearly see how error terms are modeled differently in the two models.

In an AR model, the lagged values of yt are predictors. And the error term et in the model is just like the error term in a multiple linear regression.

In an MA model, the past forecast errors are predictors.

One thing to notice is that It is possible to write any stationary AR(p) model as an infinite MA model, and an (invertible) MA(p) can be written as an infinite AR.

SAS Employee

## Re: Moving Average Errors vs. Error Correction Term (Serial Correlation)

FYI, you can find some detailed concept descriptions in http://www2.sas.com/proceedings/sugi28/252-28.pdf and the relationship between stationary AR model and MA model in https://www.otexts.org/fpp/8/4.

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