Reinforcement learning algorithms describe how an agent can learn an optimal action policy
in a sequential decision process, through repeated experience. In a given environment, the …

Equilibrium multiplicity in dynamic games: Testing and estimation | The Econometrics Journal
| Oxford Academic Skip to Main Content Advertisement Oxford Academic Journals Books …

We study the binary choice problem in a data-rich environment with asymmetric loss
functions. The econometrics literature covers nonparametric binary choice problems but …

Climate change is one of the major international environmental challenges facing nations,
and has the potential to cause catastrophic damages worldwide. International environmental …

The STZ is the core concept of the i-DREAMS project. This report aims to explicitly describe
the practical conceptualisation of the STZ to develop the theoretical framework for …

This dissertation explores two main themes: the development of static games using machine
learning, and the impact of firms' behaviors on economic and health outcomes. The first …

Thanks to the availability of more and more high-dimensional data, recent developments in
machine learning (ML) have redefined decision-making in numerous domains. However, the …

Dynamic discrete choice models are widely employed to answer substantive and policy
questions in settings where individuals' current choices have future implications. However …

In this thesis we develop robust data-driven methods for energy markets or electricity
consumption. We first focus on the generation of realistic time series with machine learning …

In recent years, software firms have migrated from the traditional licensing business model to
the" Software as a Service"(SaaS) model, where consumers subscribe to the software on …