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. Start with the practical questions to connect those concepts to an analyst’s workflow.

Practical analyst questions

• Is my data suitable for Abacus, and what must I prepare? — Data readiness, input units, missing values, scaling and identifying variation.
• Which Abacus model should I choose, and what does that choice permit? — Ordinary panel models, named estimators, the RE release restriction and supported operations.
• What do contribution, ROAS and scenario results actually mean? — Output interpretation, budget units, aggregation and comparable planning scenarios.

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:

1. Data suitability and preparation
2. Model choice and supported operations
3. Causal Identification in Marketing Mix Modelling
4. Bayesian Priors for Econometricians
5. Prior Predictive Checks for Econometricians
6. MCMC Diagnostics for Econometricians
7. Posterior Predictive Checks for Econometricians
8. Model Comparison for Econometricians
9. Contributions, ROAS and scenarios