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2021 / Konc T., I. Savin and J. van den Bergh

The social multiplier of environmental policy: application to carbon taxation

Journal of Environmental Economics and Management, 105
We analyze the effectiveness of environmental policy when consumers are subject to social influence. To this end, we build a model of consumption decisions driven by socially-embedded preferences formed under the influence of peers in a social network. This setting gives rise to a social multiplier of environmental policy. In an application to climate change, we derive Pigouvian and target-achieving carbon taxes under socially-embedded preferences. Under realistic assumptions the social multiplier is equal to 1.30, allowing to reduce the effective tax by 38%. We show that the multiplier depends on four factors: strength of social influence, initial taste distribution, network topology and income distribution. The approach provides a basis for rigorously analyzing a transition to low-carbon lifestyles and identifying complementary information and network policies to maximize the effectiveness of carbon taxation.
2020 / Bao, T., M. Hennequin, C. Hommes and D. Massaro

Coordination on bubbles in large-group asset pricing experiments

Journal of Economic Dynamics and Control, Vol. 110, Art.-Nr. 103702
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.
2020 / Castro J., Drews S., Exadaktylos F., Foramitti J., Klein F., Konc T., Savin I. and van den Bergh J.

A review of agent-based modelling of climate-energy policy

WIREs Climate Change, Vol. 11, Issue 4
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.
2020 / Pieschner S. and C. Fuchs

Bayesian inference for diffusion processes: using higher-order approximations for transition densities

Royal Society Open Science, Vol. 7, Issue 10
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.
2020 / McClintock, B.T. , R. Langrock, O. Gimenez, E. Cam, D. L. Borchers, R. Glennie and T.A. Patterson

Uncovering ecological state dynamics with hidden Markov models

arXiv preprint arXiv:2002.10497
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.
2020 / Pohle, J., R. Langrock, M. van der Schaar, R. King and F.H. Jensen

A primer on coupled state-switching models for multiple interacting time series

arXiv preprint arXiv:2004.14700
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.
2020 / Delli Gatti, D. and J. Grazzini

Rising to the challenge: Bayesian estimation and forecasting techniques for macroeconomic Agent Based Models

Journal of Economic Behavior & Organization, Vol. 178, 875-902
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.
2020 / Ruse, M.G., A. Samson and S. Ditlevsen

Inference for biomedical data by using diffusion models with covariates and mixed effects

Journal of the Royal Statistical Society, Series C: Applied Statistics, Vol. 69, Issue 1, 167-193
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.
2020 / Foramitti J., Savin I. and J. van den Bergh

Emission tax vs. permit trading under bounded rationality and dynamic markets

Energy Policy, Vol. 148, Art.-Nr. 112009
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.
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