Macroeconomist
I am a Postdoc at the Institute for Macroeconomics and Econometrics of the University of Bonn. Currently, I am directly funded by the German Reserach Foundation (I am the principal investigator of DFG Project 441540692). My research focusses on Dynamic Macroeconomics, Monetary Theory, and Inequality with emphasis on structural empirical analysis and heterogeneity. I also have a strong background in quantitative methods and computational economics.
This paper presents the Etace Virtual Appliance. The purpose of the software package is, among others, to provide researchers the possibility to explore the dynamics of the Eurace@Unibi agent-based macroeconomic model and to encourage the reproducibility and transparency of research. The package contains various components that allow the user to initialize, simulate and analyze the model. We also give a short overview of what can be done with the Etace Virtual appliance.
In brief, my current position is fully funded by the German Reserach Foundation (DFG) as part of my reserach project on nonlinear Bayesian estimations. With the OSE initiative I also successfully raised funding for teaching and research infrastructure. Prior to joining the University of Bonn I spent two years as a Postdoc at the IMFS at Goethe University Frankfurt in cooperation with the Hoover Institution at Stanford University (longer-term visits in Stanford in 2018 and 2019). I completed my PhD at the University of Amsterdam and Bielefeld University, supervised jointly by Cars Hommes and Herbert Dawid. I won the 2017 Student Price of the Society for Computational Economics. During my PhD I was financed by scholarships from the Bielefeld Graduate School in Economics and the DFG. I obtained my MSc in economics from the University of Granada (top of class) and studied economics at Humboldt University Berlin at undergraduate level. I have worked as a professional guitar player and as an IT consultant for several start-up companies.
My packages can be found on GitHub:
econpizza is a generic nonlinear solver for general equilibrium models, including heterogenous agent models (and including exressing and parsing models). It uses an alternative shooting algoritm that is faster and more robust than the extended path method implemented in Dynare. Example heterogenous agents models (and representative agent models) are provided. The package is documented in this draft.
pydsge is a Python based solution and simulation toolbox, specifically targeted to provide tools for nonlinear filtering and estimation of models with occasionally binding constraints. Its back-end for nonlinear filtering is econsieve, a hybrid between the Particle filter and the Kalman filter. Both packages are explained in the respective method paper above.
dime_sampler (Python), DIMESampler.jl (Julia) and dime-mcmc-matlab (matlab) provide the differential-independence mixture ensemble (DIME) MCMC sampler from my paper on ensemble MCMC sampling. DIME MCMC is a (very fast) global multi-start optimizer and, at the same time, a MCMC sampler that converges to the posterior distribution. This makes any posterior mode density maximization prior to MCMC sampling superfluous. The DIME sampler is pretty robust for odd shaped, multimodal distributions. DIME MCMC is parallelizable: many chains can run in parallel, and the necessary number of draws decreases almost one-to-one with the number of chains.
I maintain a compilation of unsolved problems in macroeconomics, please be invited to browse or contribute (or shoot me a short email).