I am a researcher in Econometrics, Macroeconomics and Statistics and currently lead a research group, funded by the DFG, at the University Münster. My research interests include quantitative macroeconomics, econometrics and time series analysis with a focus on the methodological development of Frequentist and Bayesian identification and estimation methods for dynamic and stochastic models with time-varying risks and rare disasters. I have taught classes at all levels in DSGE Models, Econometrics, Empirical Methods, Macroeconomics, Statistics as well as Software courses in R, MATLAB, and Dynare.

I am a Linux and open-source enthusiast and actively contribute to several projects (such as Dynare) to make my research results and developed methods accessible to practitioners and policy makers.


  • Computational Economics
  • DSGE Models
  • Econometrics
  • Macroeconomics
  • Rare Disasters
  • Time Series Analysis


  • PhD in Econometrics, 11/2015

    University Münster

  • MSc in Economics, 04/2012

    University Münster

  • BSc in Economics, 09/2009

    University Bonn



Principal Investigator

University Münster

Apr 2019 – Present Münster, Germany

DFG Project 411754673: Identification and Estimation of Dynamic Stochastic General Equilibrium Models: Skewness Matters

Responsibilities include:

  • PhD Supervision
  • Project Management
  • Research


Dynare Team

Mar 2019 – Present CEPREMAP, France
Responsibilities include:

  • Analytic Derivatives
  • Bug fixing
  • Identification Toolbox
  • GMM Estimation Toolbox


Institut der Deutschen Wirtschaft

Feb 2018 – Dec 2019 Köln, Germany
Taught courses in Introduction to R for Applied Economists.

Full Professor in Econometrics

University Münster

Oct 2017 – Sep 2018 Münster, Germany
Temporary Position. Taught courses in Econometrics, Introduction to R, Statistics, and Macroeconometrics.

Research Fellow (PostDoc)

SFB 823 at Technical University Dortmund

Nov 2015 – Jun 2017 Dortmund, Germany
Researched the transmission channels of macroeconomic shocks and economic policy with a focus on time-varying risk premia and rare disaster. Taught Survey Sampling Methods and GMM, Indirect Inference, and Bootstrap.

PhD Traineeship

European Central Bank

May 2015 – Jul 2015 Frankfurt, Germany
Research stay at DG-E Fiscal Policies. Researched fiscal policy within the EAGLE model.

Research Associate (PhD Student)

University Münster

Jun 2012 – Oct 2015 Münster, Germany
PhD Thesis on Local identification of nonlinear and non-Gaussian DSGE models. Taught courses in DSGE Models, Empirical Methods, Macroeconometrics, Multivariate Time Series Analysis, Introduction to R, GMM, Indirect Inference, and Bootstrap.

Referee Service

Jan 2012 – Present
Computational Statistics and Data Analysis, Economic Modelling, Journal of Economic Dynamics and Control, National Science Centre Poland, The B.E. Journal of Macroeconomics


Travel Grant

Best Student Award of the Economics Department

Study Grant (Bachelor and Master)

Recent Posts

Dynare user guide, tutorials, and videos

We have evaluated the results of Dynare’s user survey. One thing that came up frequently was a wish for more tutorials and examples on all the features Dynare offers. Albeit the manual covers most of it, it is a reference but not a user guide.

Install Script for Linux

I have been distro-hopping in Linux and will outline tutorials on my configurations for my Dell XPS 13 9360 and Dell Precision 7520 soon…

Installing Dynare and Matlab on Linux

I have been distro-hopping in Linux and will outline tutorials on how to install Matlab and Dynare under the different Linux distributions. Coming soon…


GMM/SMM/IRF-Matching Estimation in Dynare

In this project (joint with the Dynare Team) we plan to provide an interface for a GMM/SMM/IRF-matching toolbox in Dynare.

Skewness Matters

This project investigates the impact of skewness on the identifiability and estimability of parameters in linear and nonlinear DSGE models using new statistical distributions and econometric methods.

Recent & Upcoming Presentations

Assessing the strength of parameter identification in empirically relevant DSGE Models - Which posterior sampling method performs best?

We show that weak identification is a serious concern in empirically relevant DSGE models with many nominal, real and financial …

Assessing the strength of parameter identification in empirically relevant DSGE Models - Which posterior sampling method performs best?

We show that weak identification is a serious concern in empirically relevant DSGE models with many nominal, real and financial …

Identification analysis and higher-order approximation of DSGE models

Lecture on theory and current development of identification toolbox in Dynare for linear and nonlinear DSGE models.


I try to make my work publicly available and reproducible. As publishing an open-access paper in a journal can be prohibitively expensive, please feel free to contact me for a personal copy of my papers.

The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models

The decisions a researcher makes at the model building stage are crucial for parameter identification. This paper contains a number of applied tips for solving identifiability problems and improving the strength of DSGE model parameter identification by fine-tuning the (1) choice of observables, (2) functional specifications, (3) model features and (4) choice of structural shocks. We offer a formal approach based on well-established diagnostics and indicators to uncover and address both theoretical (yes/no) identifiability issues and weak identification from a Bayesian perspective. The concepts are illustrated by two exemplary models that demonstrate the identification properties of different investment adjustment cost specifications and output-gap definitions. Our results provide theoretical support for the use of growth adjustment costs, investment-specific technology, and partial inflation indexation.

Higher-order statistics for DSGE models

Closed-form expressions for unconditional moments, cumulants and polyspectra of order higher than two are derived for non-Gaussian or nonlinear (pruned) solutions to DSGE models. Apart from the existence of moments and white noise property no distributional assumptions are needed. The accuracy and utility of the formulas for computing skewness and kurtosis are demonstrated by three prominent models, the baseline medium-sized New Keynesian model used for empirical analysis (first-order approximation), a small-scale monetary business cycle model (second-order approximation) and the neoclassical growth model (third-order approximation). Both the Gaussian as well as Student’s t-distribution are considered as the underlying stochastic processes. Lastly, the efficiency gain of including higher-order statistics is demonstrated by the estimation of a RBC model within a Generalized Method of Moments framework.

Identification of DSGE models - The effect of higher-order approximation and pruning

This paper shows how to check rank criteria for a local identification of nonlinear DSGE models, given higher-order approximations and pruning. This approach imposes additional restrictions on (higher-order) moments and polyspectra, which can be used to identify parameters that are unidentified in a first-order approximation. The identification procedures are demonstrated by means of the Kim (2003) and the An and Schorfheide (2007) models. Both models are identifiable with a second-order approximation. Furthermore, analytical derivatives of unconditional moments, cumulants and corresponding polyspectra up to fourth order are derived for the pruned state-space.