Refereed publications

Hedging Against Inflation: Housing versus Equity [open access][code files], Empirical Economics 65 (6) (2023), December 2023, pp. 2583 – 2626.

Housing and the Business Cycle Revisited [accepted manuscript][online appendix], Journal of Economic Dynamics and Control 99 (2019), February 2019, pp. 103-115.

Working Papers

Polynomial chaos expansion: Efficient evaluation and estimation of computational models (with Christopher Heiberger and Johannes Huber) [code files].

Polynomial chaos expansion (PCE) provides a method that enables the representation of a random variable, the quantity of interest (QoI), as a series expansion of other random variables, the inputs. Traditionally, uncertain parameters of the model are treated as random inputs, and the QoI is an element of the model’s solution, e.g., the policy function, the second moments of observables, or the posterior kernel. PCE then surrogates time-consuming repetition of model solutions and evaluations for different values of the inputs. Additionally, PCE allows to discretize the space of square-integrable distributions, including those containing mass points. The paper discusses the suitability of PCE for computational economics. We, therefore, introduce to the theory behind PCE, analyze the convergence behavior for different elements of the solution of the standard real business cycle model as illustrative example, and check the accuracy, if standard empirical methods are applied. The results are promising, both in terms of accuracy and efficiency.

Hone the Neoclassical Lens and Zoom in on Germany’s Fiscal Stimulus Program 2008-2009 (with Johannes Huber) [version: Business cycle accounting for the German fiscal stimulus program during the Great Recession].

Business Cycle Accounting (BCA) by Chari, Kehoe, and McGrattan (2007, Econometrica) completes the “…through the lens of a neoclassical model”-approach. This paper refines and extends the methodology in four primary dimensions, creating a manual. i) the choice of the level of aggregation is critical and thus must be case-dependent. ii) a strict distinction between growth and cycle is beneficial. iii) BCA requires Maximum-Likelihood, even if it is difficult. Given these difficulties, we introduce a procedure that reliably and quickly locates the maximum and enables a detailed evaluation of the likelihood function and robustness checks. iv) it is revealing to discuss the results in the context of economic and political events. To illustrate the necessity and benefits of the refinements, we apply BCA to the Great Recession in Germany. The main driver was efficiency, followed by net exports and distortions in the markets for business investments. Government consumption and durable consumption acted counter-cyclically. We attribute the latter to a high subsidy for new cars or, more generally, for durables.


A neoclassical tale of two Germanys, with Vasilij Konysev (working title, 2 Papers)

We compile national accounts for the regions of the former separated West and East Germanies from 1960 to 2019. The data includes expenditure accounts, capital accounts (physical and human), and hours worked. We apply the neoclassical model to create dimensionless allocation and productivity efficiency measures. Therefore, the analysis avoids hitherto unsolved problems with missing information on purchasing power parity conversion rates. Further, we introduce quantity constraint wedges to account for the amount of consumption goods variety and unemployment. For the segregated East German economy, we find excessive inflows, inputs of hours worked, and human capital accumulation, yet not physical. Due to lower total factor productivity growth, East Germany’s economic activity fell ten years behind from 1960 to 1989 compared to West Germany. After reunification, we observe convergence in most measures or already initial similarity between the reunified West and East Germany, except for the productivity in the tradable goods sector. Despite convergence, net inflows to East Germany remain high—accounting together with the tradable goods sector’s productivity gap for the 33% points lower GDP per capita in Eastern Germany.