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Barro‐type endogenous growth models propose a nonmonotonic relationship between productive public spending and growth. Under this so‐called nonlinearity hypothesis the size and direction of growth effects due to an increase in public spending depend on the share of public spending in GDP. Employing German time‐series data we examine the validity of the nonlinearity hypothesis. We estimate growth effects by using models whose coefficients are allowed to vary with the share of public spending in GDP. Our results support the hypothesis for public consumption but not for public investment data. (JEL H54, E62, C22)

Area-wide measurements of traffic flow are usually not possible with today's common sensor technologies. However, such information is essential for (urban) traffic planning and control. Hence, in order to support traffic managers, this paper analyses an approach for deriving traffic flows from probe vehicle speeds, which are potentially available with a wide spatial coverage. The idea is to apply the speed-flow function as known from macroscopic traffic flow theory. In this context, a stochastic representation of the fundamental diagram via Bayesian networks is proposed which also considers the temporal dependencies and transitions between the appearing traffic states. The paper describes the relevant theoretical concepts in comparison to the traditional approach of fitting deterministic curves to empirical speed-flow relations. Moreover, it analyses the findings of an extensive validation in context of traffic flow estimation via probe vehicle data using real traffic measurements provided by about 600 local detectors and about 4,300 taxi probes in Berlin, Germany.

Continuous wheel condition monitoring is indispensable for the early detection of wheel defects. In this paper, we provide an approach based on cepstral analysis of axle-box accelerations (ABA). It is applied to the data in the spatial domain, which is why we introduce a new data representation called navewumber domain. In this domain, the wheel circumference and hence the wear of the wheel can be monitored. Furthermore, the amplitudes of peaks in the navewumber domain indicate the severity of possible wheel defects. We demonstrate our approach on simple synthetic data and real data gathered with an on-board multi-sensor system. The speed information obtained from fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU) data is used to transform the data from time to space. The data acquisition was performed with a measurement train under normal operating conditions in the mainline railway network of Austria. We can show that our approach provides robust features that can be used for on-board wheel condition monitoring. Therefore, it enables further advances in the field of condition based and predictive maintenance of railway wheels.

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