I am a Postdoc at the University of Bonn. My research focuses on Dynamic Macroeconometrics, Monetary Theory, and Inequality with emphasis on structural empirical analysis and heterogeneity. I also have a strong background in IT and computational methods and am advocating free and open source software jointly with the OSE initiative. My current research statement can be found here.

Working Papers

US Business Cycle Dynamics at the Zero Lower Bound (R&R at the Journal of Applied Econometrics)

[current version, WP version, with Felix Strobel, posterior & historic shocks, code ] (read more)

Using a novel nonlinear Bayesian likelihood approach that fully accounts for the lower bound on nominal interest rates, we analyze US post-crisis macroeconomic dynamics and provide reference parameter estimates. We find that despite the attention received in the literature, neither the inclusion of financial frictions nor that of household heterogeneity improves the empirical fit of the standard model or its ability to provide a joint explanation for the post-2007 dynamics. Associated financial shocks mis-predict an increase in consumption. We illustrate that the common practice of omitting the ZLB period in the estimation severely distorts the analysis of the most recent economic dynamics.

Monetary Policy and Speculative Asset Markets (R&R at the European Economic Review)

[current version, WP version, code ]
(read more)

I study monetary policy in an estimated financial New-Keynesian model extended by behavioral expectation formation in the asset market. Credit frictions create a feedback between asset markets and the macroeconomy, and behaviorally motivated speculation can amplify fundamental swings in asset prices, potentially causing endogenous, nonfundamental bubbles and bursts. Booms in asset prices improve firms financing conditions and are therefore deflationary. These features greatly improve the power of the model to replicate empirical key moments. A monetary policy that targets asset prices can dampen financial cycles and reduce volatility in asset markets (dampening effect). This comes at the cost of creating an additional channel through which asset price fluctuations transmit to macroeconomic fundamentals (spillover effect). I find that unless financial markets are severely overheated, the undesirable fluctuations in inflation and output caused by the spillover effect more than outweigh the benefits of the dampening effect.

A Structural Investigation of Quantitative Easing (under review)

[current version, WP version, with Gavin Goy and Felix Strobel, code ] (read more)

We provide evidence that the Federal Reserve's large-scale asset purchases actually reduce inflation. Using nonlinear Bayesian methods that fully account for the binding zero lower bound (ZLB), we estimate a macro-finance DSGE model. Counterfactual analysis suggests that by easing financing conditions, quantitative easing facilitated an increase in aggregate investment. The resulting expansion of firms’ production capacities lowered their marginal costs. These disinflationary supply side effects dominated over the inflationary effects coming from the higher aggregate demand. At the ZLB, the concomitant rise in real interest rates in turn induced a net fall in aggregate consumption.

Rational vs. Irrational Beliefs in a Complex World (under review)

[current version, WP version, with Cars Hommes, code ] (read more)

Can boundedly rational agents survive competition with fully rational agents? We develop a highly nonlinear heterogeneous agents model with rational forward looking versus boundedly rational backward looking agents and evolving market shares depending on their relative performance. Our novel numerical solution method detects equilibrium paths characterized by complex bubble and crash dynamics. Boundedly rational trend-extrapolators amplify small deviations from fundamentals, while rational agents anticipate market crashes after large bubbles and drive prices back close to fundamental value. Overall rational and non-rational beliefs co-evolve over time, with time-varying impact, and their interaction produces complex endogenous bubble and crashes, without any exogenous shocks.

The Hockey Stick Phillips Curve and the Zero Lower Bound (under review)

[current version, WP version, with Philipp Lieberknecht, code ] (read more)

We show that the interplay between the zero lower bound (ZLB) and the costs of external financing generates an observational disconnect between inflation and output. In normal times, factor costs dominate firms' marginal costs; credit spreads and the nominal interest rate balance out. When nominal rates are constrained, larger spreads can more than offset the effect of lower factor costs on firms' price setting. The Phillips curve is hence flat at the ZLB, but features a positive slope in normal times and thus an overall hockey stick shape. This supply-side mechanism also weakens the effects of forward guidance on inflation.

Efficient Solution and Computation of Models with Occasionally Binding Constraints (under review)

[current version, WP version, code ] (read more)

Structural macroeconometric analysis and new HANK-type models with extremely high dimensionality require fast and robust methods to efficiently deal with occasionally binding constraints (OBCs), especially since major developed economies have again hit the zero lower bound on nominal interest rates. This paper shows that a linear dynamic rational expectations system with OBCs, depending on the expected duration of the constraint, can be represented in closed form. Combined with a set of simple equilibrium conditions, this can be exploited to avoid matrix inversions and simulations at runtime for significant gains in computational speed. An efficient implementation is provided in Python programming language. Benchmarking results show that for medium-scale models with an OBC, more than 150,000 state vectors can be evaluated per second. This is an improvement of more than three orders of magnitude over existing alternatives. Even state evaluations of large HANK-type models with almost 1000 endogenous variables require only 0.1 ms.

Can Taxation Predict US-Top-Wealth Share Dynamics?

[current version, WP version, with Thomas Fischer] (read more)

We show that the degree of capital gains taxation can retrace the dynamics of wealth inequality in the US since the 1920s. Precisely matching up- and downturns and levels of top shares, it has high overall explanatory power. This result is drawn from an estimated and micro-founded portfolio-choice model where idiosyncratic return risk and disagreement in expectations on asset returns generate an analytically tractable fat-tailed Pareto distribution for the top-wealthy. This allows us to decompose the sample into periods of transient and stationary wealth concentration. The model generates good out-of-sample forecasts. As an addition we predict the future evolution of inequality for different tax regimes.

Current Projects

The Quantitative Effects of Taxation on Inequality Dynamics

[draft upon request] (read more)

We use a novel identification strategy of functional-coefficient structural inference to estimate the relationship between different forms of taxation and the concentration of income and wealth. We find that overall, the degree of taxation has a very high explanatory power on the dynamics of top shares, in particular so the series of income taxes. This regularity holds for the US, UK, France and Sweden. We estimate that an 1 percent increase of taxation reduces the concentration of wealth in the long run by approximately 0.5 percent and concentration of income by 0.25 percent. This effect is more emphasized in the US and less so for Sweden.

The Macroeconomic Effects of QE in the Euro Area: Evidence from a Structural Estimation

[work in progress, with Gavin Goy]

(draft coming soon)

Policy, Media and Other Work

The Federal Reserve and quantitative easing: A boost for investment, a burden on inflation

[ VoxEU August 2020, with Gavin Goy and Felix Strobel

Solution, Filtering and Estimation of Models with the ZLB

[Methods summary, code ]

The ETACE Virtual Appliance: An Exploratory for the Eurace@Unibi model

[with Sander van der Hoog, download paper, download software] (read more)

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.


Download CV Download research statement

In brief, I spent two years as a Postdoc at the IMFS at Goethe University Frankfurt in cooperation with the Hoover Institution at Stanford University. During this time, I visited Stanford twice during the winter 2018/19 and 2019/20. Before that, 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 a scholarship from the Bielefeld Graduate School in Economics and Management. Before, I was holding a scholarship from the German Research Foundation. 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.


I am an enthusiast user of Arch Linux and Python. For many reasons I support the use of free and open source software in science. As such, I think that the access to software must be unrestrained by expensive and restrictive licenses. Implementations should be tractable, and libraries easily extensible. Openness is a booster for performance and flexibility. I am in particular sceptical towards closed ecosystems like Matlab and think it is a barrier towards future scientific advancement. As code is getting more and more important, I advertise code code sharing and cooperation on new implementations. To that end I encourage the use of style guides (they can also be automatized) and version control systems. Most of my codes can be found in my repositories on github .

My packages on :

pydsge econsieve grgrlib pynare

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. pynare is a Python wrapper of Dynare that also provides access to its workspace. Its declared goal is to allow working with Dynare from within Python without having to lay hands on Matlab/Octave (the package is currently unmaintained).

From 2017 to 2020 I was coordinating the refactoring of the Macroeconomic Model Database (MMB) to meet modern standards and to become independent of proprietary software.

During my time at Bielefeld University I created the ETACE Virtual Appliance (jointly with P. Harting and S. van der Hoog). Here is the installation guide, a user manual and a licenses file. Downlad the ETACE-VA here.

Useful Stuff

I maintain a compilation of unsolved problems in macroeconomics, please be invited to browse or contribute (or shoot me a short email).

Macro Puzzles

Some useful econ-related links: On econometrics:
  • This interactive online textbook (by Roger Labbe) gives an excellent and hands-on introduction into Bayesian filtering.
  • This post explains very nicely how the Hamiltonian Monte Carlo (HMC) Sampler works and, en passant, shows why using Metropolis Hastings might not be a good idea for many problems in practice. Note that HMC is also behind the NUTS sampler used in Stan (a widely used sampling package), but not very feasible for many applications in structural (macro-)econometrics. The reason is that HMC requires the evaluation of the gradient at each draw, which is relatively costly for most of our likelihood functions. Have a look at emcee if you are looking for a powerful multi-purpose sampler.
On programming: Finally, some handy Python packages:
  • numba - probably by now the first address if your code is too slow.
  • dolo (by Pablo Winant) is a powerful collection of many tools to solve and run (macro) economic models.
  • (also by Pablo Winant) provides fast-as-light interpolation tools.
  • filterpy (by Roger Labbe, see above) is a collection of linear and nonlinear Bayesian filters.
  • emcee provides very powerful and easily paralellizable MCMC sampler.
  • chaospy - for quasi-random numbers and uncertainty quantification.


mail [ät] gregorboehl [döt] com
gboehl [ät] uni-bonn [döt] de
Dr. Gregor Boehl
University of Bonn
Adenauerallee 24-42
53113 Bonn