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Murad Yusifov: Modelling the inflationary processes and forecasting:an application of ARIMA,
SARIMA models

Graph.1. Residuals ACF and PACF functions

.8
Autocorrelation -.4
.4
.0

-.8
2 4 6 8 10 12 14 16 18 20 22 24
Actual Theoretical

Partial autocorrelation .8

.4

.0

-.4
2 4 6 8 10 12 14 16 18 20 22 24
Actual Theoretical

-statistics points that there is no auto correlation. As it seems from the correlogram the are

some spikes in the 1st and 12th lags.(See.Annex1.Graph.2.)

Let`s look at the results obtained from the application of ARIMA. Constructed

multiplicative SARIMA(1,0,1)(0,0,1)x12 model is as follows.

(12)

Table.1. SARIMA(1,0,1)(0,0,1)x12
Variable Coefficients t-Statistics Prob.
C 100.2804 688.0120 0.0000
[0.145754]
AR(1) 1.347068 6.295969 0.0000
[0.213957]
AR(2) -0.468569 -3.501751 0.0008
[0.133810]
MA(1) -0.845638 -4.538298 0.0000
[0.186334]
SMA(12) 0.454063 3.358619 0.0013
[0.135193]

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