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## Box-Jenkins Differencing vs. ARIMA Estimation

This example shows how to estimate an ARIMA model with nonseasonal integration using estimate. The series is not differenced before estimation. The results are compared to a Box-Jenkins modeling strategy, where the data are first differenced, and then modeled as a stationary ARMA model (Box et al., 1994).

The time series is the log quarterly Australian Consumer Price Index (CPI) measured from 1972 to 1991.

Load and plot the Australian CPI data.

load Data_JAustralian
Y = Dataset.PAU;
N = length(Y);

figure
plot(Y)
xlim([0,N])
set(gca,'XTick',1:10:N);
set(gca,'XTickLabel',datestr(dates(1:10:N),17));
title('Log Quarterly Australian CPI')


The series is nonstationary, with a clear upward trend. This suggests differencing the data before using a stationary model (as suggested by the Box-Jenkins methodology), or fitting a nonstationary ARIMA model directly.

Step 2. Estimate an ARIMA model.

Specify an ARIMA(2,1,0) model, and estimate.

model = arima(2,1,0);
fit = estimate(model,Y);


ARIMA(2,1,0) Model:
--------------------
Conditional Probability Distribution: Gaussian

Standard          t
Parameter       Value          Error       Statistic
-----------   -----------   ------------   -----------
Constant      0.0100723    0.00328015        3.07069
AR{1}       0.212059     0.0954278        2.22219
AR{2}       0.337282      0.103781        3.24994
Variance    9.23017e-05   1.11119e-05        8.30659


The fitted model is

where is normally distributed with standard deviation 0.01.

The signs of the estimated AR coefficients correspond to the AR coefficients on the right side of the model equation. In lag operator polynomial notation, the fitted model is

with the opposite sign on the AR coefficients.

Step 3. Difference the data before estimating.

Take the first difference of the data. Estimate an AR(2) model using the differenced data.

dY = diff(Y);
modAR = arima(2,0,0);
fitAR = estimate(modAR,dY);


ARIMA(2,0,0) Model:
--------------------
Conditional Probability Distribution: Gaussian

Standard          t
Parameter       Value          Error       Statistic
-----------   -----------   ------------   -----------
Constant      0.0104289    0.00380427        2.74137
AR{1}       0.201194      0.101463        1.98293
AR{2}        0.32299      0.118035         2.7364
Variance    9.42421e-05   1.16259e-05        8.10622


The parameter point estimates are very similar to those in Step 2. The standard errors, however, are larger when the data is differenced before estimation.

Forecasts made using the fitted AR model will be on the differenced scale. Forecasts made using the ARIMA model in Step 2 will be on the same scale as the original data.

References:

Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. Time Series Analysis: Forecasting and Control. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.