We propose two novel methods to “bring ABMs to the data”. First, we put forward a new 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...

We propose two novel methods to "bring ABMs to the data". First, we put forward a new 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...

Environmental managers face substantial uncertainty when deciding on management actions. To reduce this uncertainty prior to decision-making, collecting new data may help arrive at more informed decisions. Whether any resulting improvement in the decision will outweigh the cost of collecting the...

Environmental managers face substantial uncertainty when deciding on management actions. To reduce this uncertainty prior to decision-making, collecting new data may help arrive at more informed decisions. Whether any resulting improvement in the decision will outweigh the cost of collecting the...

We propose the relaxation algorithm as a simple and powerful method for simulating the transition process in growth models. This method has a number of important advantages: (1) It can easily deal with a wide range of dynamic systems including stiff differential equations and systems giving rise...

The transition paths from plan to market have varied markedly across countries. Central and Eastern European and the Baltic countries, which opted for a fast and profound transformation of their institutions including business climates, rapidly narrowed the productivity gap with advanced...

Common integrated assessment models produce the counterintuitive result that higher risk aversion does not lead to stronger near-term abatement. This paper re-examines this result with a DICE model that is fully coupled with a thermohaline circulation model. It also features Epstein-Zin utility...

In this paper we use the property that certainty equivalence, as implied by a first-order approximation to the solution of stochastic discrete-time models, breaks in its equivalent continuous-time version. We study the extent to which a first-order approximated solution built by perturbation...

We argue that deep learning provides a promising avenue for taming the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges posed by solving dynamic equilibrium models, especially the feedback loop between individual agents' decisions and the aggregate...

In this paper we use the property that certainty equivalence, as implied by a first-order approximation to the solution of stochastic discrete-time models, breaks in its equivalent continuous-time version. We study the extent to which a first-order approximated solution built by perturbation...