Hannan-Quinn Criterion

The Hannan-Quinn Criterion is a statistical tool used to determine the best model for analyzing data, especially in time-series analysis. It helps decide how many past data points to include when making predictions or understanding patterns. The criterion balances goodness of fit with simplicity, penalizing overly complex models that may fit the current data well but perform poorly on new data. Compared to other criteria, Hannan-Quinn is more conservative than some but less so than others, aiming for an optimal trade-off between accuracy and simplicity to improve the reliability of results.