Publications

Summary

Financial constraints have an important impact on the development of eco-innovations but their effect varies according to the type of funding. This article studies the interaction between public funding on the one hand, and internal and external lack of funding on the other. The empirical analysis is based on a sample of European small- and medium-sized enterprises, and exploits information on firms’ involvement in eco-innovation activities, their drivers, and obstacles. Our results show that, even accounting for demand-pull effects and regulatory interventions, access to public funds and fiscal incentives is effective for improving the firm’s ability to introduce eco-innovations, particularly if the company has ample funds from either internal or external sources. Our findings suggest also that public funding is perceived by firms as complementary to other external finance.

Summary

We present a large-group experiment in which participants predict the price of an asset, whose realization depends on the aggregation of individual forecasts. The markets consist of 21 to 32 participants, a group size larger than in most experiments. Multiple large price bubbles occur in six out of seven markets. The bubbles emerge even faster than in smaller markets. Individual forecast errors do not cancel out at the aggregate level, but participants coordinate on a trend-following prediction strategy that gives rise to large bubbles. The observed price patterns can be captured by a behavioral heuristics switching model with heterogeneous expectations.

Summary

Agent‐based models (ABMs) have recently seen much application to the field of climate mitigation policies. They offer a more realistic description of micro behavior than traditional climate policy models by allowing for agent heterogeneity, bounded rationality and nonmarket interactions over social networks. This enables the analysis of a broader spectrum of policies. Here, we review 61 ABM studies addressing climate‐energy policy aimed at emissions reduction, product and technology diffusion, and energy conservation. This covers a broad set of instruments of climate policy, ranging from carbon taxation, and emissions trading through adoption subsidies to information provision tools such as smart meters and eco‐labels. Our treatment pays specific attention to behavioral assumptions and the structure of social networks. We offer suggestions for future research with ABMs to answer neglected policy questions.

Summary

Modelling random dynamical systems in continuous time, diffusion processes are a powerful tool in many areas of science. Model parameters can be estimated from time-discretely observed processes using Markov chain Monte Carlo (MCMC) methods that introduce auxiliary data. These methods typically approximate the transition densities of the process numerically, both for calculating the posterior densities and proposing auxiliary data. Here, the Euler–Maruyama scheme is the standard approximation technique. However, the MCMC method is computationally expensive. Using higher-order approximations may accelerate it, but the specific implementation and benefit remain unclear. Hence, we investigate the utilization and usefulness of higher-order approximations in the example of the Milstein scheme. Our study demonstrates that the MCMC methods based on the Milstein approximation yield good estimation results. However, they are computationally more expensive and can be applied to multidimensional processes only with impractical restrictions. Moreover, the combination of the Milstein approximation and the well-known modified bridge proposal introduces additional numerical challenges.

Summary

Ecological systems can often be characterised by changes among a set of underlying states pertaining to individuals, populations, communities, or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or" hidden". Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that would otherwise be intractable. However, while HMMs are routinely applied in other disciplines, they have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, and review the immense scope of HMMs for applied ecological research. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.

Summary

State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying non-observable states. Coupled state-switching models extend these approaches to address the case of multiple observation sequences whose underlying state variables interact. In this paper, we provide an overview of the modelling techniques related to coupling in state-switching models, thereby forming a rich and flexible statistical framework particularly useful for modelling correlated time series. Simulation experiments demonstrate the relevance of being able to account for an asynchronous evolution as well as interactions between the underlying latent processes. The models are further illustrated using two case studies related to a) interactions between a dolphin mother and her calf as inferred from movement data; and b) electronic health record data collected on 696 patients within an intensive care unit.

Summary

We propose two novel methods to “bring Agent Based Models (ABMs) to the data”. First, we describe a Bayesian procedure to estimate the numerical values of ABM parameters that takes into account the time structure of simulated and observed time series. Second, we propose a method to forecast aggregate time series using data obtained from the simulation of an ABM. We apply our methodological contributions to a specific medium-scale macro ABM.

Summary

Neurobiological data such as electroencephalography measurements pose a statistical challenge due to low spatial resolution and poor signal‐to‐noise ratio, as well as large variability from subject to subject. We propose a new modelling framework for this type of data based on stochastic processes. Stochastic differential equations with mixed effects are a popular framework for modelling biomedical data, e.g. in pharmacological studies. Whereas the inherent stochasticity of diffusion models accounts for prevalent model uncertainty or misspecification, random‐effects model intersubject variability. The two‐layer stochasticity, however, renders parameter inference challenging. Estimates are based on the discretized continuous time likelihood and we investigate finite sample and discretization bias. In applications, the comparison of, for example, treatment effects is often of interest. We discuss hypothesis testing and evaluate by simulations. Finally, we apply the framework to a statistical investigation of electroencephalography recordings from epileptic patients. We close the paper by examining asymptotics (the number of subjects going to ∞) of maximum likelihood estimators in multi‐dimensional, non‐linear and non‐homogeneous stochastic differential equations with random effects and included covariates.

Summary

A price on emissions can be achieved through an emission tax or permit trading. The advantages and drawbacks of either instrument are debated. We present an agent-based model to compare their performance under bounded rationality and dynamic markets. It describes firms that face uncertainty about future demand and prices; use heuristic rules to decide production levels, trading prices, and technology adoption; and are heterogeneous in terms of production factors, abatement costs, and trading behavior. Using multiple evaluation criteria and a wide range of parameter values, we find that the main difference between the two policies lies in the fact that permit prices fall after successful abatement. This can lead to higher production levels under permit trading, but can also drive emission-efficient firms out of the market. Scarcity rents under permit trading can further create higher profit rates for firms, the extent of which is shown to depend on the mechanisms for market-clearing and initial allocation.