Willi MutschlerUniversity of Tuebingen | EKU Tübingen · Faculty of Economics and Social Sciences
Willi Mutschler
Professor
About
11
Publications
677
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78
Citations
Introduction
My research interests 🔬 include quantitative macroeconomics 💹 and econometrics 🧮 with a focus on developing identification and estimation methods 🔮 for dynamic and stochastic models with skewed distributions 🪝 and rare disasters 🌪️. I am also a member of Dynare’s 📈 core development team and maintain the identification and method-of-moments toolboxes 🧰.
Additional affiliations
April 2021 - present
Position
- Professor (Assistant)
Description
- Assistant Professor in International Macroeconomics. Responsibilities include Research, Teaching, PhD Supervision.
April 2019 - March 2021
Position
- Principal Investigator
Description
- DFG Project 411754673: Identification and Estimation of Dynamic Stochastic General Equilibrium Models: Skewness Matters. Responsibilities include: PhD Supervision, Project Management, Research
March 2019 - present
Dynare Team
Position
- Developer
Description
- Member of core development team. Maintain the identification and method-of-moments toolboxes.
Education
June 2012 - November 2015
October 2009 - April 2012
October 2006 - September 2009
Publications
Publications (11)
The Dynare Manual is the best place to go to understand the Dynare syntax, commands, and options.
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...
Both the investment adjustment costs parameters in Kim (2003) and the monetary policy rule parameters in An & Schorfheide (2007) are locally not identifiable. We show means to dissolve this theoretical lack of identification by looking at (1) the set of observed variables, (2) functional specifications (level vs. growth costs, output-gap definition...
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...
This thesis adds to the literature on the local identification of nonlinear and non-Gaussian DSGE models. It gives applied researchers a strategy to detect identification problems and means to avoid them in practice. A comprehensive review of existing methods for linearized DSGE models is provided and extended to include restrictions from higher-or...
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...
Kim (2003, JEDC) shows functional equivalence between intertemporal and multisectoral investment adjustments costs in a linearized RBC model. From an identification point of view, two parameters are not separately distinguishable, they enter as a sum into the linearized solution. We demonstrate that estimating the quadratic approximation of the mod...
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 pro...
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 pro...
