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Post Closed as "Not suitable for this site" by jbowman, kjetil b halvorsen♦, Ferdi, mdewey, Alexis
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David
David
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I have some data that varies with time and some that stays constant (e.g., location, race stay constant). Is it possible to implement a mixed time-varying coefficient model in python? What I mean is:

$$ y = \beta_0(t) \cdot x_0 + \beta_1(t) \cdot x_1 + \beta_2 \cdot x_3 + \beta_3 $$

where $\beta_0, \beta_1$ are time-varying (dependent on $t$), and the rest of the betas are not.

This is similar to the SAS package TVEM (page 7, eq. 4 of this docthis doc).

It seems this might be possible in R using the gam models, but I'm not very familiar with R. Any clues to approach this in python (or get me started) would be helpful. I'm familiar with the statsmodels package and the scipy stack.

I have some data that varies with time and some that stays constant (e.g., location, race stay constant). Is it possible to implement a mixed time-varying coefficient model in python? What I mean is:

$$ y = \beta_0(t) \cdot x_0 + \beta_1(t) \cdot x_1 + \beta_2 \cdot x_3 + \beta_3 $$

where $\beta_0, \beta_1$ are time-varying (dependent on $t$), and the rest of the betas are not.

This is similar to the SAS package TVEM (page 7, eq. 4 of this doc).

It seems this might be possible in R using the gam models, but I'm not very familiar with R. Any clues to approach this in python (or get me started) would be helpful. I'm familiar with the statsmodels package and the scipy stack.

I have some data that varies with time and some that stays constant (e.g., location, race stay constant). Is it possible to implement a mixed time-varying coefficient model in python? What I mean is:

$$ y = \beta_0(t) \cdot x_0 + \beta_1(t) \cdot x_1 + \beta_2 \cdot x_3 + \beta_3 $$

where $\beta_0, \beta_1$ are time-varying (dependent on $t$), and the rest of the betas are not.

This is similar to the SAS package TVEM (page 7, eq. 4 of this doc).

It seems this might be possible in R using the gam models, but I'm not very familiar with R. Any clues to approach this in python (or get me started) would be helpful. I'm familiar with the statsmodels package and the scipy stack.

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David
David
  • 123
  • 1
  • 5

Fitting a time-varying coefficient model in python

I have some data that varies with time and some that stays constant (e.g., location, race stay constant). Is it possible to implement a mixed time-varying coefficient model in python? What I mean is:

$$ y = \beta_0(t) \cdot x_0 + \beta_1(t) \cdot x_1 + \beta_2 \cdot x_3 + \beta_3 $$

where $\beta_0, \beta_1$ are time-varying (dependent on $t$), and the rest of the betas are not.

This is similar to the SAS package TVEM (page 7, eq. 4 of this doc).

It seems this might be possible in R using the gam models, but I'm not very familiar with R. Any clues to approach this in python (or get me started) would be helpful. I'm familiar with the statsmodels package and the scipy stack.