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Meridian supports national-level models, although we recommend that you
use the geo-level model when possible. The national-level model is a special
case of the geo-level model with only a single geo. There is no separate model
class.
The following parameter restrictions apply, and are automatically enforced, when
you use the national-level model:
\(\eta^{[M]}_i=0\) and \(\beta_{1,i}^{[M]}=\beta_i^{[M]} \ \ \forall i\)
\(\eta^{[OM]}_i=0\) and \(\beta_{1,i}^{[OM]}=\beta_i^{[OM]} \ \ \forall i\)
\(\eta^{[RF]}_i=0 \) and \(\beta^{[RF]}_{g,i}=\beta^{[RF]}_i
\ \ \forall i\)
\(\eta^{[ORF]}_i=0 \) and \(\beta^{[ORF]}_{g,i}=\beta^{[ORF]}_i
\ \ \forall i\)
\(\xi_i^{[C]}=0\) and \(\gamma_{1,i}^{[C]}=\gamma_i^{[C]} \ \ \forall c\)
unique_sigma_for_each_geo = False
National-level versus geo-level modeling
Statistical modeling relies on identifying repeatable patterns in data, and this
can be done much more effectively with geo-level data, assuming that the
patterns are reasonably similar across geos.
The geo-level model pools data across geos to increase the effective sample
size. It provides tighter credible intervals, provided the geos are similar in
terms of the media impact mechanism as the model assumes. For more information
see Geo-level Bayesian Hierarchical Media Mix
Modeling.
Geo-level data also improves estimates for time-effects (such as trend and
seasonality), due to the fact that there are multiple observations per time
period to use for estimation. Geo-level data can support the use of more knots
to model the \(\mu_t\) parameter. Often it is reasonable to use one knot per
time period for maximum flexibility. However, national-level data has fewer
degrees of freedom to spare for time-effects. For example, one knot per time
period would completely saturate the model.
The advantages of a geo-level model are so strong, that if only national-level
data is available for relatively few channels, we recommend imputing the
national-level data across geos. See Geo-selection and national-level data for more details.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-02-24 UTC."],[[["Meridian's geo-level model is generally recommended over the national-level model for its enhanced accuracy and statistical power."],["The national-level model is essentially a simplified version of the geo-level model with a single geographical unit and specific parameter restrictions."],["Geo-level modeling leverages data across multiple geographical areas, leading to improved estimates, tighter credible intervals, and better identification of temporal patterns."],["If only national-level data is available, consider replicating it across geographical units within the geo-level model to enhance results."],["Geo-level modeling offers greater flexibility for modeling time-dependent effects due to increased data points and observations."]]],["Meridian supports national-level models as a special case of geo-level models, which are recommended for their efficacy. National-level models enforce parameter restrictions, including setting specific eta and beta values to zero or equal across all 'i'. Geo-level modeling pools data across geos, improving sample size and estimate accuracy for time-effects. Using national data across geos is suggested when only national-level data with limited channels is available. Population scaling is not applicable for national-level.\n"]]
