Torben Klarl

On The Numerical Accuracy Of First-Order Approximate Solutions To Dsge Models

Many algorithms that provide approximate solutions for dynamic stochastic general equilibrium (DSGE) models employ the QZ factorization because it allows a flexible formulation of the model and exempts the researcher from identifying equations that give raise to infinite eigenvalues. We show, by means of an example, that the policy functions obtained by this approach may differ from both the solution of a properly reduced system and the solution obtained from solving the system of nonlinear equations that arises from applying the implicit function theorem to the models equilibrium conditions. As a consequence, simulation results may depend on the specific algorithm used and on the numerical values of parameters that are theoretically irrelevant. The sources of this inaccuracy are ill-conditioned matrices as they emerge, e.g., in models with strong habits. Researchers should be aware of those strange effects, and we propose several ways to handle them.

Knowledge Diffusion and Knowledge Transfer: Two Sides of the Medal

Understanding the way in which knowledge is technically produced and transferred, and how its diffusion path can be characterized is of fundamental importance for the performance of an economy. Although this fact seems to be plausible ex ante, the relevant literature so far has paid less attention investigating the microeconomic link between knowledge transfer and knowledge diffusion in a comprehensive approach. The aim of this paper is to highlight the link between knowledge transfer, knowledge diffusion and network effects in a stochastic environment, because the adoption decision of new knowledge should be treated as a stochastic event. For this reason, a new knowledge diffusion model in the line of Bass (1969) has been put forward, which integrates knowledge diffusion and knowledge transfer. The advantage of the proposed model is twofold. From a theoretical point of view, not only the so-called unimodal diffusion phenomena can be modelled, but also bimodal diffusion phenomena can be obtained. From an empirical point of view, the model which incorporates heteroscedastic errors and mean reverting behaviour can be theoretically estimated directly within a standard SUR context.

Spatial Model Selection and Spatial Knowledge Spillovers: A Regional View of Germany

The aim of this paper is to introduce a new model selection mechanism for cross sectional spatial models. This method is more flexible than the approach proposed by Florax et al. (2003) since it controls for spatial dependence as well as for spatial heterogeneity. In particular, Bayesian and Maximum-Likelihood (ML) estimation methods are employed for model selection. Furthermore, higher order spatial influence is considered. The proposed method is then used to identify knowledge spillovers from German NUTS-2 regional data. One key result of the study is that spatial heterogeneity matters. Thus, robust estimation can be achieved by controlling for both phenomena.