FAQ
This section collects longer-form answers to recurring MMM, Bayesian, and
panel-econometrics questions that come up when practitioners move from
classical econometrics to PanelMMM.
The pages are written for technical readers who already understand regression, panel data, and causal inference, but want the Abacus framing.
Core model concepts
- Adstock and Saturation for Econometricians — Why modern MMM uses carryover and diminishing-returns transformations instead of log-linear shortcuts.
- Causal Identification in Marketing Mix Modelling — What an MMM can and cannot identify causally from observational data.
- HSGP (Hilbert Space Gaussian Process) for Econometricians — How flexible time-varying effects map to familiar basis and shrinkage ideas.
- Baseline vs Media Trade-Offs in MMM — Why baseline terms and media terms can trade attribution against each other even when fit quality looks similar.
Priors and model checking
- Bayesian Priors for Econometricians — How priors relate to constraints, penalties, and regularisation you already use in classical work.
- Prior Predictive Checks for Econometricians — How to test whether your configured priors imply plausible behaviour before fitting.
- Posterior Predictive Checks for Econometricians — How to judge whether the fitted model reproduces the data well enough to trust downstream interpretation.
Computation and comparison
- MCMC Diagnostics for Econometricians — How to read trace plots, R-hat, ESS, and divergences.
- Model Comparison for Econometricians — How to think about ELPD, LOO-CV, posterior predictive checks, and their limitations.
Panel specification
- Do We Need a Mundlak Specification Test in Abacus? — Why Abacus uses posterior inspection of CRE terms rather than a frequentist Mundlak test.
Suggested reading order
If you are new to Bayesian MMM, a practical sequence is: