Postestimation

Postestimation refers to the set of practices performed after a statistical model has been estimated. It encompasses diagnostic checks, interpretation of estimates, validation of model assumptions, and preparation of results for reporting. It is applicable across many modeling frameworks, including linear and generalized linear models, time-series, survival, and mixed models.

Common activities include residual analysis (plots of residuals vs fitted, Q-Q plots), assessment of assumptions (linearity, homoskedasticity, independence, normality), goodness-of-fit and information criteria (R-squared, AIC, BIC, log-likelihood), and inference via standard errors, confidence intervals, and hypothesis tests. For nonlinear models, postestimation often involves computing marginal effects or predicted probabilities. Analysts also examine influence and leverage (Cook’s distance), multicollinearity diagnostics, and robustness checks with alternative specifications or robust standard errors.

In statistical software, postestimation commands are run after model fitting to extract results such as predicted values, residuals, residual plots, postestimation tests, and fit statistics. Examples include generating margins or average marginal effects, conducting postestimation tests of assumptions, and reporting diagnostic statistics.

The purpose is to validate the adequacy of the model, aid interpretation, and guide model refinement or reporting. Postestimation steps must be interpreted with caution to avoid over-fitting or inappropriate inference, particularly when multiple tests are performed. It complements the estimation phase and is a standard part of rigorous data analysis.