Welcome to respyabc’s documentation!¶
respyabc is a package that uses a likelihood-free inference framework to infer parameters from dynamic discrete choice models. Inference is conducted using Approximate Bayesian Computing and a Sequential Monte-Carlo algorithm via pyABC. Models must be simulated via respy. Currently, only the model of Keane and Wolpin (1994) is implemented. The extension to further models is the next step of the development phase.
The package has been built and is maintained by Manuel Huth within the scope of the courses Effective Programming Practices for Economists and Scientific Computing, which are taught within the University of Bonn’s Master in Economics.
With conda available on your path, installing
respyabc is as simple as typing
$ pip install pyabc
$ conda config --add channels conda-forge
$ conda install -c opensourceeconomics respy
$ conda install -c manuhuth respyabc
Examples¶
Check out the tutorials on this website or you can find an example project that showcases how respyabc can be used in an actual research paper in this repository.
OSE grading
Api Reference