Some of our projects aim to facilitate the research workflow itself and make it more reproducible. These will be found in the section Workflow Tools. Others try to facilitate research by providing methodological tools and graphical assistance. These will be found in the section Methodological Tools. Many of these tools are used in our research. In the section Research Software we list packages that implement economic models.

Workflow Tools


pytask is a workflow management system which facilitates reproducible data analyses. Its features include: automatic discovery of tasks, a debug mode, repetition of a task with different inputs, and many more. Further, it is easily extensible with plugins. Plugins are available for parallelization, LaTeX, Stata, R, Julia and more. (Documentation)


With pytask as it’s backbone, econ-project-templates aims to provide project templates for economists that make it easy to produce reproducible research using one or more of the most frequently used programming languages in economics (i.e., Python, Stata, R, Julia). (Documentation)

Methodological Tools


estimagic is a Python package to fit large-scale empirical models to data and make inferences about the estimated model parameters. It is especially suited to solve difficult optimization problems. It 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. It provides tools for nonlinear optimization, numerical differentiation, and statistical inference. (Documentation)


pybaum provides tools to work with pytrees which is a concept borrowed from JAX. Pytree’s refer to tree-like structures built out of container-like Python objects. We use pybaum in several of our packages. One notable example is estimagic. (Documentation)


dags provides tools to create executable dags from interdependent functions. (Documentation)


econsive is a collection of nonlinear Bayesian filters, in particular for high dimensional models. The filters are implemented in python. It provides the Transposed-Ensemble Kalman Filter (TEnKF) for state and likelihood inference, and the Nonlinear Path-Adjusting Smoother (NPAS) for exact smoothed states. (Documentation)

Research Software


sid is an agent-based simulation model for infectious diseases like COVID-19. It scales from simple examples to complex models which makes it an ideal tool for prototyping, educational purposes, and research. (Documentation)

Gabler J., Raabe T., Röhrl K. & von Gaudecker H. (2022) The Effectiveness of Strategies to Contain SARS-CoV-2: Testing, Vaccinations, and NPIs, arXiv preprint arXiv:2106.11129, submitted.


respy is an open-source framework written in Python for the simulation and estimation of some finite-horizon discrete choice dynamic programming models. The group of models which can be currently represented in respy are called Eckstein–Keane–Wolpin models. (Documentation)

Eisenhauer P., Gabler J. & Janys L. (2021) Structural models for policy-making: Coping with parametric uncertainty, arXiv preprint arXiv:2103.01115, submitted.


pydsge is a Python package that allows to simulate, filter, and estimate DSGE models with occasionally binding constraints. As such, it allows to conduct full-blown Bayesian estimations (including Bayesian filtering) of macroeconomic models featuring an endogenous zero lower bound on nominal interest rates. (Documentation)

Boehl G. (2022) Efficient Solution and Computation of Models with Occasionally Binding Constraints, working paper.


skillmodels a Python implementation of estimators for skill formation models. (Documentation)


ruspy is an open-source package for the simulation and estimation of a prototypical infinite-horizon dynamic discrete choice model based on Rust (1987). (Documentation)

Blesch M. & Eisenhauer P. (2021) Robust decision-making under risk and ambiguity, arXiv preprint arXiv:2104.12573, submitted


grmpy is an open-source Python package for the simulation and estimation of the generalized Roy model. It serves as a teaching tool to promote the conceptual framework of the generalized Roy model, illustrate a variety of issues in the econometrics of policy evaluation, and showcases basic software engineering practices. (Documentation)

Archived Packages