fulton_statsmodels_2017
State Space Estimation of Time Series Models in Python: Statsmodels
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Jupyter Notebook
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: https://github.com/ChadFulton/fulton_statsmodels_2017/raw/master/fulton_statsmodels_2017_v1.pdf
Notebooks
There are three Jupyter notebooks with code showing maximum likelihood and Bayesian estimation of three example models:
Build
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, runpython 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