Allow exogenous regressors in BayesianVARMAX
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Pull Request Overview
This PR adds support for exogenous regressors to the BayesianVARMAX model via observation intercepts. The implementation allows for flexible configuration where exogenous variables can be shared across all endogenous states or specified individually for each state.
Key changes include:
- Added
exog_state_namesandk_exogparameters to support different exogenous variable configurations - Implemented data handling and parameter creation for exogenous regression coefficients
- Added comprehensive validation logic for parameter combinations
- Extended test coverage for all exogenous variable configuration scenarios
Reviewed Changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| pymc_extras/statespace/models/VARMAX.py | Core implementation adding exogenous regressor support with parameter validation, data handling, and symbolic graph construction |
| tests/statespace/models/test_VARMAX.py | Comprehensive test suite covering all exogenous variable configuration cases and error conditions |
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AlexAndorra
approved these changes
Aug 26, 2025
andreacate
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Aug 27, 2025
* First pass on exogenous variables in VARMA * Adjust state names for API consistency * Allow exogenous variables in BayesianVARMAX * Eagerly simplify model where possible * Typo fix
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This PR adds support for exogenous variables in
BayesianVARMAXvia the observation intercept. Same deal as SARIMAX -- it's implemented in a way that minimizes the number of states in the system.Unlike Statsmodels, we have support for each observed state to have its own regressors by passing a dictionary. If you just pass a list, everyone shares the same data.
Example 1: Everyone shares data, data names are auto-assigned
Example 2: Everyone shares data, data names are provided
Example 3: Each observes state (endogenous state) has its own exogenous regressors. y3 has no exogenous regressors at all, so it is omitted from the dictionary. It just so happens that each observed has its own regressors, but they could also share all or a subset.