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...

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...

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...

Score-driven models have been applied in some 400 published articles over the last decade. Much of this literature cites the optimality result in Blasques et al. (2015), which, roughly, states that sufficiently small score-driven updates are unique in locally reducing the Kullback-Leibler (KL)...

Score-driven models have been applied in some 400 published articles over the last decade. Much of this literature cites the optimality result in Blasques et al. (2015), which, roughly, states that sufficiently small score-driven updates are unique in locally reducing the Kullback-Leibler (KL)...