Note: respy is not under development anymore and only inactively maintained since 2021. Check out our GitHub organization to find projects that are currently under development.
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 (Aguirregabiria and Mira (2010))
What makes respy powerful is that it allows to build and solve structural models in weeks or months whose development previously took years. The design of respy allows the researcher to flexibly add the following components to her model.
- Any number of discrete choices (e.g., working alternatives, schooling, home production, retirement) where each choice may yield a wage, may allow for experience accumulation and can be constrained by time, a maximum amount of accumulated experience or other characteristics.
- Condition the decision of individuals on its previous choices or their labor market history.
- Adding a finite mixture with any number of subgroups to account for unobserved heterogeneity among individuals as developed by Keane and Wolpin (1997).
- Any number of time-constant observed state variables (e.g., ability measures (Bhuller et al. (2020)), race (Keane and Wolpin (2000)), demographic variables) found in the data.
- Correct the estimation for measurement error in wages, either using a Kalman filter in maximum likelihood estimation or by adding the measurement error in simulation based approaches.
You can install respy via conda with
$ conda config --add channels conda-forge
$ conda install -c opensourceeconomics respyPlease visit our online documentation for tutorials and other information.
As respy relies heavily on pandas, you might also want to install their
recommended dependencies to speed up internal calculations done with
pd.eval.
conda install -c conda-forge bottleneck numexprrespy was completely rewritten in the second release and evolved into a general framework for the estimation of Eckstein-Keane-Wolpin models. Please cite it with
@Unpublished{Gabler2020,
Title = {respy - A Framework for the Simulation and Estimation of
Eckstein-Keane-Wolpin Models.},
Author = {Janos Gabler and Tobias Raabe},
Year = {2020},
Url = {https://github.com/OpenSourceEconomics/respy},
}
Before that, respy was developed by Philipp Eisenhauer and provided a package for the simulation and estimation of a prototypical finite-horizon discrete choice dynamic programming model. At the heart of this release is a Fortran implementation with Python bindings which uses MPI and OMP to scale up to HPC clusters. It is accompanied by a pure Python implementation as teaching material. If you use respy up to version 1.2.1, please cite it with
@Software{Eisenhauer2019,
Title = {respy - A Package for the Simulation and Estimation of a prototypical
finite-horizon Discrete Choice Dynamic Programming Model.},
Author = {Philipp Eisenhauer},
Year = {2019},
DOI = {10.5281/zenodo.3011343},
Url = {https://doi.org/10.5281/zenodo.3011343}
}
We appreciate citations for respy because it helps us to find out how people have been using the package and it motivates further work.
Aguirregabiria, V., & Mira, P. (2010). Dynamic Discrete Choice Structural Models: A Survey. Journal of Econometrics, 156(1), 38-67
Bhuller, M., Eisenhauer, P. and Mendel, M. (2020). The Option Value of Education. Working Paper.
Keane, M. P. and Wolpin, K. I. (1994). The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence. The Review of Economics and Statistics, 76(4): 648-672.
Keane, M. P. and Wolpin, K. I. (1997). The Career Decisions of Young Men. Journal of Political Economy, 105(3): 473-522.
Keane, M. P., & Wolpin, K. I. (2000). Eliminating Race Differences in School Attainment and Labor Market Success. Journal of Labor Economics, 18(4), 614-652.