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Empirical Dynamic Modeling Stata Package

Package Description

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Empirical Dynamic Modeling (EDM) is a way to perform causal analysis on time series data. The edm Stata package implements a series of EDM tools, including the convergent cross-mapping algorithm.

Key features of the package:

  • powered by a fast multi-threaded C++ backend,
  • able to process panel data, a.k.a. multispatial EDM,
  • able to handle missing data using new dt algorithms or by dropping points,
  • factor variables can be added to the analysis,
  • multiple distance functions available (Euclidean, Mean Absolute Error, Wasserstein),
  • GPU acceleration available.

Installation

To install the stable version directly through Stata:

ssc install edm, replace

To install the latest development version, first install the stable version from SSC then inside Stata run:

edm update, development replace

The source code for the package is available on Github.

R & Python packages

We are currently creating the fastEDM R package and the fastEDM Python package which are direct ports of this Stata package to R & Python. As all the packages share the same underlying C++ code, their behaviour will be identical.

Other Resources

This site serves as the primary source of documentation for the package, though there is also:

  • our Stata Journal paper which explains the package and the overall causal framework, and
  • Jinjing's QMNET seminar on the package, the recording is on YouTube and the slides are here.
  • Patrick's short presentation on the EDM packages to the Time Series and Forecasting Symposium 2022:

Authors

Citation

Jinjing Li, Michael J. Zyphur, George Sugihara, Patrick J. Laub (2021), Beyond Linearity, Stability, and Equilibrium: The edm Package for Empirical Dynamic Modeling and Convergent Cross Mapping in Stata, Stata Journal, 21(1), pp. 220-258

@article{edm-stata,
  title={Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in {S}tata},
  author={Li, Jinjing and Zyphur, Michael J and Sugihara, George and Laub, Patrick J},
  journal={The Stata Journal},
  volume={21},
  number={1},
  pages={220--258},
  year={2021},
}