Information criteria are statistical tools used for model selection that help determine how well a model fits the data while penalizing for complexity. These criteria balance goodness of fit with model simplicity, allowing analysts to identify models that generalize well to new data. They are especially relevant in regression analysis with time series data, where the risk of overfitting can be significant due to the inclusion of many predictors or complex models.
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