Box-Jenkins Model

Box-Jenkins Model

A methodology that makes forecasts of future economic behavior by applying a best fit model to past behavior. In using the model, one applies an autoregressive moving average to past data. In order to do this accurately, one must identify the data being measured and ensure it is independent of other variables, define the parameters of investigation, and check the model. The model is used in econometrics.
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Analysis of the historical data is used to identify an appropriate Box-Jenkins model y looking at their ACF, PACF, and stationary and seasonality behavior if any.
Box-Jenkins Model. The Box-Jenkins (BJ) approach models the system as shown in Figure 5.
The world cotton price forecasting by using Box-Jenkins model. Tekirdag J.
When the value of the current output [Y.sub.t] depends solely on p prior outputs and the current input (random shock) [e.sub.t], the Box-Jenkins model takes the form of
ARIMA model is known as Box-Jenkins model [4] which includes AR and MA models identified by Box-Jenkins methodology.
* Step 1 * Tentative identification: Book borrowing data are used to tentatively identify an appropriate Box-Jenkins model.
The ARIMA model (Autoregressive Integrated Moving Average), known also as the Box-Jenkins Model, uses only stationary series (mean and variance are constant).
Data obtained through the Box-Jenkins model, which were applied to develop index forecasting for trading volume of options on futures contracts, tend to show a decrease in the value question.
For each time interval (hour or a part of it), the value of the deviation [[zeta].sub.t] is found by Box-Jenkins model. If the difference [v.sub.t] - [[zeta].sub.t] is suitable according to the previous criterion, equals [v.sub.t] - [[zeta].sub.t] equals [[xi].sub.t], and peak component value 0.
[5]; Appendix) was formulated for the Box-Jenkins model developed in step 2.
The Box-Jenkins model resembles the OLS model with a few technical embellishments.
The box-jenkins model includes several major steps.