- Define a parameter vector that holds the unique parameters you want to share across different time steps for the D matrix.
- Create a mapping function that, given a time step, returns the appropriate D matrix with NaNs for the errors that do not occur at that time step and the shared parameter values for the errors that do occur.
- During the optimization or estimation process, ensure that the mapping function is called at each time step to construct the correct D matrix.
Define a state space model where some parameters are time varying and others are not
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Hi!
I am trying to estimate a state space model with ssm. I have defined matrices A, B and C as the function asks (A and B are time invariant and C is time varying).
However, I still have not found a way to implement my observation-innovation coefficient matrix, D, as I wish.
I want a time varying D, where 5 of the 6 errors only occur sporadically, but, when they occur, the associated parameter should be the same.
But when doing so in ssm it counts each Nan as a different parameter. I am not sure ParamMap works as C and D are time varying. If someone knows how to solve this or has a hint towards implementing it, I would be very grateful!
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Aditya
2024 年 1 月 17 日
When you have time-varying matrices like C and D in your state space model, you can typically handle them by defining a function that generates these matrices for each time step. However, if you're trying to have shared parameters for non-zero entries in D that occur sporadically, you'll need a more customized solution.
Here's a general approach you might consider:
To implement this in ssm, you might need to subclass one of the existing models or write your own custom model class that can handle this behavior.
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