Induced innovation and associated issues of path dependence and inertia are of critical importance in the transition to a carbon free economy. We develop a model that, instead of modeling these processes themselves, models the implications of these characteristics and in the process allows us to...

We explore how optimal emission abatement trajectories are affected by dynamic characteristics of greenhouse-gas emitting systems, such as inertia, induced innovation, and pathdependency, by formulating a compact and analytically tractable model with stylized damage assumptions to derive the...

The Shared Socioeconomic Pathways (SSPs) were developed as a framework for exploring alternative futures with challenges for climate change mitigation and adaptation. Whilst originally developed at the global scale, the SSPs have been increasingly interpreted at the national scale in order to...

We use Google search data with the aim of predicting unemployment, CPI and consumer confidence for the US, UK, Canada, Germany and Japan. Google search queries have previously proven valuable in predicting macroeconomic variables in an in-sample context. To our knowledge, the more challenging...

This article presents a new filter for state-space models based on Bellman's dynamic programming principle applied to the posterior mode. The proposed Bellman filter generalises the Kalman filter including its extended and iterated versions, while remaining equally inexpensive computationally....

We compute joint sovereign default probabilities as coincident systemic risk indicators. Instead of commonly used CDS spreads, we use government bond yield data which provide a longer data history. We show that for the more recent sample period 2008-2015, joint default probabilities based on CDS...

Classic real options theory rests on two debatable assumptions: projects require a fixed investment and generate cash flows that follow a random walk. Relaxing both assumptions leads to radically different conclusions regarding the optimal timing of investment. We model investment using a...

Urban structures and urban growth rates are highly persistent. This has far-reaching implications for the optimal size and timing of new construction. We prove that rational developers postpone construction not because prospects are gloomy, but because they are bright. The slow mean reversion in...

We propose a novel observation-driven modeling framework that allows for time variation in the model’s parameters using a proximal-parameter (ProPar) update. The ProPar update is the solution to an optimization problem that maximizes the logarithmic observation density with respect to the...