Detecting model misspecification involves several diagnostic techniques:
Residual Analysis: Examining the residuals (differences between observed and predicted values) can indicate whether the model captures the data well. Goodness-of-Fit Tests: Statistical tests such as the chi-square test can assess how well the model fits the data. Cross-Validation: Using different subsets of the data to validate the model can reveal overfitting or underfitting issues. Sensitivity Analysis: Assessing how sensitive the model results are to changes in assumptions or input parameters can indicate robustness.
