How to interpret the result of AIC-BIC test?
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Hi all,
I wanted to set the value of p,q or p,d,q for an ARIMA model using following code, even though I don't know how to interpret the obtained results, so any help would be highly appreciated.
LOGL = zeros(4,4);
PQ = zeros(4,4);
for p = 0:3
for q = 0:3
mod = arima(p,1,q);
[fit,~,logL] = estimate(mod,z,'print',false);
LOGL(p,q) = logL;
PQ(p,q) = p+q;
end
end
LOGL = reshape(LOGL,16,1);
PQ = reshape(PQ,16,1);
[aic,bic] = aicbic(LOGL,PQ+1,100);
mAIC=reshape(aic,4,4)
mBIC=reshape(bic,4,4)
output:
mAIC =
1.0e+04 *
-1.4059 -1.6748 -1.9129 -2.0337
-3.2659 -3.0362 -3.0430 -3.0458
-1.4044 -3.0381 -3.0379 -3.0377
-1.4042 -3.0379 -2.7176 -2.7174
mBIC =
1.0e+04 *
-1.4057 -1.6743 -1.9121 -2.0326
-3.2654 -3.0354 -3.0419 -3.0445
-1.4036 -3.0370 -3.0366 -3.0361
-1.4032 -3.0366 -2.7160 -2.7155
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Karanjot
2023-9-29
Hi Hamed,
I understand that you want to learn about interpreting results of AIC-BIC test.
Information criteria rank models using measures that balance goodness of fit with parameter parsimony. For a particular criterion, models with lower values are preferred. Akaike's Information Criterion (AIC) provides a measure of model quality obtained by simulating the situation where the model is tested on a different data set. After computing several different models, you can compare them using this criterion.
The mAIC matrix represents the AIC values for different combinations of p and q, while the mBIC matrix represents the BIC values.
The AIC and BIC are used as model selection criteria in statistics. Lower values indicate better-fitting models. In this case, you can compare the AIC and BIC values within each matrix to identify the combination of p and q that provides the best fit for your ARIMA model.
To learn more about this, please refer to the pages below, especially the ‘More About’ section:
I hope this helps!
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