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Next, use statistical tools to help you identify the optimal lag structure. The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are commonly used for this purpose. Both criteria estimate the goodness of fit of a model while penalizing the use of additional parameters. This means that they favor models that fit the data well with the fewest number of lags. The lower the AIC or BIC value, the better the model. Compare the AIC and BIC values for models with different lag structures. The model with the lowest values of these criteria is typically the one that provides the best balance between fit and parsimony.
