Model Specification for Custom ARIMA Models
The Model tab allows you to specify the structure of a custom ARIMA model.
ARIMA Orders. Enter values for the various ARIMA components of your model into the corresponding cells of the Structure grid. All values must be non-negative integers. For autoregressive and moving average components, the value represents the maximum order. All positive lower orders will be included in the model. For example, if you specify 2, the model includes orders 2 and 1. Cells in the Seasonal column are only enabled if a periodicity has been defined for the active dataset (see "Current Periodicity" below).
- Autoregressive (p). The number of autoregressive orders in the model. Autoregressive orders specify which previous values from the series are used to predict current values. For example, an autoregressive order of 2 specifies that the value of the series two time periods in the past be used to predict the current value.
- Difference (d). Specifies the order of differencing applied to the series before estimating models. Differencing is necessary when trends are present (series with trends are typically nonstationary and ARIMA modeling assumes stationarity) and is used to remove their effect. The order of differencing corresponds to the degree of series trend--first-order differencing accounts for linear trends, second-order differencing accounts for quadratic trends, and so on.
- Moving Average (q). The number of moving average orders in the model. Moving average orders specify how deviations from the series mean for previous values are used to predict current values. For example, moving-average orders of 1 and 2 specify that deviations from the mean value of the series from each of the last two time periods be considered when predicting current values of the series.
Seasonal Orders. Seasonal autoregressive, moving average, and differencing components play the same roles as their nonseasonal counterparts. For seasonal orders, however, current series values are affected by previous series values separated by one or more seasonal periods. For example, for monthly data (seasonal period of 12), a seasonal order of 1 means that the current series value is affected by the series value 12 periods prior to the current one. A seasonal order of 1, for monthly data, is then the same as specifying a nonseasonal order of 12.
Current Periodicity. Indicates the periodicity (if any) currently defined for the active dataset. The current periodicity is given as an integer--for example, 12 for annual periodicity, with each case representing a month. The value None is displayed if no periodicity has been set. Seasonal models require a periodicity. You can set the periodicity from the Define Dates dialog box.
Dependent Variable Transformation. You can specify a transformation performed on each dependent variable before it is modeled.
- None. No transformation is performed.
- Square root. Square root transformation.
- Natural log. Natural log transformation.
Include constant in model. Inclusion of a constant is standard unless you are sure that the overall mean series value is 0. Excluding the constant is recommended when differencing is applied.
To Specify a Custom ARIMA Model
This feature requires the Forecasting option.
- From the menus choose:
- On the Variables tab, select ARIMA for Method.
- Click Criteria....