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.
PhD in Econometrics, 11/2015
MSc in Economics, 04/2012
BSc in Economics, 09/2009
DFG Project 411754673: Identification and Estimation of Dynamic Stochastic General Equilibrium Models: Skewness Matters
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 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.
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.
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.