Publications

Summary

Diving behaviour of narwhals is still largely unknown. We use Hidden Markov models (HMMs) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. We try several HMMs with 2, 3 or 4 states, and with independent and dependent log-normal and gamma distributions, respectively, and different covariates to characterize dive patterns. In particular, diurnal patterns in diving behaviour is inferred, by using periodic B-splines with boundary knots in 0 and 24 hours.

Summary

We introduce a methodology to estimate the determinants of the formation of technology diffusion networks from the patterns of technology adoption. We apply this methodology to wind energy, which is one of the key technologies in climate change mitigation. Our results emphasize that, in particular, long-term relationships as measured by economic integration are key determinants of technological diffusion. Specific support measures are less relevant, at least to explain the extensive margin of diffusion. Our results also highlight that the scope of technological diffusion is much broader than what is suggested by the consideration of CDM projects alone, which are particularly focused on China and India. Finally, the network of technological diffusion inferred from our approach highlights the central role of European countries in the diffusion process and the absence of large hubs among developing countries.

Summary

In financial markets, participants locally optimize their profit which can result in a globally unstable state leading to a catastrophic change. The largest crash in the past decades is the bankruptcy of Lehman Brothers which was followed by a trust-based crisis between banks due to high-risk trading in complex products. We introduce information dissipation length (IDL) as a leading indicator of global instability of dynamical systems based on the transmission of Shannon information and apply it to the time series of USD and EUR interest rate swaps (IRS). We find in both markets that the IDL steadily increases toward the bankruptcy, then peaks at the time of bankruptcy and decreases afterwards. Previously introduced indicators such as ‘critical slowing down’ do not provide a clear leading indicator. Our results suggest that the IDL may be used as an early-warning signal for critical transitions even in the absence of a predictive model.