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".
A PDF version of the paper can be found in the repository, and also at: https://github.com/ChadFulton/fulton_statsmodels_2017/raw/master/fulton_statsmodels_2017_v1.pdf
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/sourcefor the reStructuredText files of text
paper/source/sections/codefor 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 run_all.pyin that directory.
notebooksfor 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 run_all.py >>> cd ../../../ >>> make html >>> make latex