estimagic is a Python package that provides high-quality and user-friendly tools to fit large scale empirical models to data and make inferences about the estimated model parameters. It is especially suited to solve difficult constrained optimization problems.

estimagic provides several advantages over similar packages, including a unified interface that supports a large number of local and global optimization algorithms and the possibility of monitoring the optimization procedure via a beautiful interactive dashboard.

estimagic provides tools for nonlinear optimization, numerical differentiation and statistical inference.

• estimagic wraps all algorithms from scipy.optimize and many more become available when installing optional dependencies.
• estimagic can automatically implement many types of constraints via reparametrization, with any optmizer that supports simple box constraints.
• estimagic encourages name-based parameters handling. Parameters are specified as pandas DataFrames that can have any kind of single or MultiIndex. This is especially useful when specifying constraints.
• The complete history of parameters and function evaluations are saved in a database for maximum reproducibility and displayed in real time via an interactive dashboard.

• estimagic can calculate precise numerical derivatives using Richardson extrapolations.
• Function evaluations needed for numerical derivatives can be done in parallel with pre-implemented or user provided batch evaluators.

• estimagic provides asymptotic standard errors for maximum likelihood and method of simulated moments.
• estimagic also provides bootstrap confidence intervals and standard errors. Of course the bootstrap procedures are parallelized.

The package can be installed via conda. To do so, type the following commands in a terminal:

$ conda config --add channels conda-forge
$ conda install -c opensourceeconomics estimagic

The first line adds conda-forge to your conda channels. This is necessary for conda to find all dependencies of estimagic. The second line installs estimagic and its dependencies.

The documentation is hosted (on rtd)

If you use Estimagic for your research, please do not forget to cite it. Many people worked on this software and you should recognize their effort.

@Unpublished{Gabler2020,
  Title  = {A Python Tool for the Estimation of (Structural) Econometric Models.},
  Author = {Janos Gabler},
  Year   = {2020},
  Url    = {https://github.com/OpenSourceEconomics/estimagic}
}

Estimagic is still in alpha status and the API might still change. We will try to keep the API more stable When we switch versions 0.0.x to 0.x.x. Until then we want to achieve the following things: