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.
#How to use eviews 10 series#
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”. It will tests up to 5 breaks in the data and show you the. There are 5 methods but go with the first one. Now go in the stability test you have multiple break-point test.
Next, determining the order of our autoregressive and moving average components. To find the structural break you have to estimate AR (1) model in this the independent variable in the lag of dependent variable. 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.
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