Consequently, there are two possible moving average components MA(1) and MA(3).
![eviews 4 download free eviews 4 download free](https://m.media-amazon.com/images/I/41wU4RARsuL._AC_.jpg)
We can see that lags 1, and 3 exceed the confidence bands. Next, to determine the order of the moving average component (“q”), we have to observe the Autocorrelation column (ACF). For the purpose of this example, I will only consider an AR(1) component. Looking at the correlogram, the first lag is a highly significant AR(1) component, and then lags 2 and 3 are on the line and could be tested. The values that exceed the band suggest the possible order of the autoregressive component. In the column, we observe a confidence band on the sides. In order to determine the order of the autoregressive component (“p”), we have to observe the partial autocorrelation column (PACF). The aim of this step is to find all the possible models to estimate. We are displaying the correlogram in the first differences because we have confirmed that “CPI” is stationary in the first differences. To identify the order of the autoregressive and moving average components, we will focus on the correlogram of “CPI” in the first differences. If our variable is non stationary in levels, we need to apply the appropriate transformations (logs/differences) to make it stationary. Why? Our series needs to be stationary in order to forecast it. We have to begin our analysis by checking for stationarity. In our example, we are trying to fit an ARIMA model for the series “ consumer price index – USA“. In other words, on stage 1 we will determine “p”, “d” and “q”. Then there is a EViews University Edition for 49.95 with a 6-months license.
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However there is an EViews Student Version Lite that is free for university students, with a license that expires after one year. Next, determining the order of our autoregressive and moving average components. The most current professional version is EViews 10 and all output in this tutorial was created using EViews 10.
![eviews 4 download free eviews 4 download free](https://i1.wp.com/img.freepik.com/free-vector/vintage-microphone-light-background-poster_1284-5301.jpg)
We are first checking for stationarity of our variable of interest. ARIMA is written as ARIMA(p,d,q) where “p” is the order of the autoregressive component, “d” is the times we need to differentiate the variable to achieve stationarity, and “q” is the order of the moving average element.