Dynamical Factor Models (DFM) Implementation (GSOC 2025) #446
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Thanks for the feedback! I'm still exploring the best approach for implementing Dynamic Factor Models. |
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Some tests are failing due to missing constants. You might have lost some changes in the reset/rebasing process |
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In the notebook a comparison between the custom DFM and the implemented DFM (which has an hardcoded version of make_symbolic_graph, that work just in this case)
…pymc_extras/statespace/models/structural/components/regression.py
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pymc_extras/statespace/models/DFM.py
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| factor_order : int | ||
| Order of the VAR process for the latent factors. If 0, the factors are treated as static (no dynamics). | ||
| Therefore, the state vector will include one state per factor and "factor_ar" will not exist. |
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When you say "no dynamics" do you mean the estimated factors will literally be static, or just that they won't be autoregressive?
I guess I'm asking if they still get stochastic innovations when factor_order = 0
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Yes sorry, maybe that was a bit misleading. The factor won't be autoregressive, but will still have stochastic innovation
pymc_extras/statespace/models/DFM.py
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| Names of the exogenous variables. If not provided, but `k_exog` is specified, default names will be generated as `exog_1`, `exog_2`, ..., `exog_k`. | ||
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| shared_exog_states: bool, optional | ||
| Whether exogenous latent states are shared across the observed states. If True, there will be only one set of exogenous latent |
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What do you mean by "exogenous latent state"? The learned regression coefficient states?
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Yes, they are the betas coefficient
Dynamical Factor Models (DFM) Implementation
This PR provides a first draft implementation of Dynamical Factor Models as part of my application proposal for the PyMC GSoC 2025 project. A draft of my application report can be found at this link.
Overview
DFM.pywith initial functionalityCurrent Status
This implementation is a work in progress and I welcome any feedback
Next Steps