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I'm working on inflation forecasting with quarterly inflation data for 1960 to 2022 using ARIMA model. The initial data is non-stationary. After the first differencing, based on the results of the p-value, it shows that the data is stationary. Then plot the ACF and PACF after first differencing, as attached pictures. With the graph of ACF an

I'm working on inflation forecasting with quarterly inflation data for 1960 to 2022 using ARIMA model. The initial data is non-stationary. After the first differencing, based on the results of the p-value, it shows that the data is stationary. Then plot the ACF and PACF after first differencing, as attached pictures. With the graph of ACF an

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
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I'm working on inflation forecasting with quarterly inflation data for 1960 to 2022 using ARIMA model. The initial data is non-stationary. After the first differencing, based on the results of the p-value, it shows that the data is stationary. Then plot the ACF and PACF after first differencing, as attached pictures.
With the graph of ACF and PACF, how can I define p, d, q for my ARIMA (p, d, q) model?
Thankyou

 

0.20 0.10 of D.inflation 0.00 Autocorrelations -0.10 -0.20 -0.30 20 Lag Bartlett's formula for MA(q) 95% confidence bands 0 10 30 40
Transcribed Image Text:0.20 0.10 of D.inflation 0.00 Autocorrelations -0.10 -0.20 -0.30 20 Lag Bartlett's formula for MA(q) 95% confidence bands 0 10 30 40
0.20 of D.inflation 0.00 Partial autocorrelations -0.20 -0.40 10 95% Confidence bands [se = 1/sqrt(n)] 20 Lag 30 40
Transcribed Image Text:0.20 of D.inflation 0.00 Partial autocorrelations -0.20 -0.40 10 95% Confidence bands [se = 1/sqrt(n)] 20 Lag 30 40
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