State Space Estimation of Time Series Models in Python: Statsmodels
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Estimating time series models by state space methods in Python: Statsmodels

This repository houses the source and Python scripts producing the paper "Estimating time series models by state space methods in Python: Statsmodels".

Paper PDF

A PDF version of the paper can be found in the repository, and also at:


There are three Jupyter notebooks with code showing maximum likelihood and Bayesian estimation of three example models:


The paper is written using Sphinx. In particular, see:

  • paper/source for the reStructuredText files of text
  • paper/source/sections/code for all of the code that is referenced in the text and that produces the output and figures. To run all code and produce all output, run python in that directory.
  • notebooks for Jupyter notebooks that flesh out the three examples in the paper (ARMA(1, 1), local level, and a simple real business cycle model)

To build the paper, in a terminal from the base directory, you must:

>>> cd paper/source/sections/code
>>> python
>>> cd ../../../
>>> make html
>>> make latex