Séminaire

Abstract: We analyze an epidemiological model in which individuals trade the costs and benefits of social distancing while being uncertain about the dynamics of the epidemic. We characterize the unique symmetric equilibrium and show that uncertainty can be the cause of an additional wave of infections. We calibrate our model to the COVID-19 pandemic and simulate the dynamics of the epidemic to illustrate the impact of uncertainty on social distancing. We show that uncertainty about the epidemic dynamics can be welfare improving, both in terms of fraction of deaths and average payoffs.

Abstract
A significant challenge in climate change negotiations is establishing a burden-sharing method that all or most governments find fair. Two key fairness principles are emphasized by the United Nations Framework Convention on Climate Change in allocating mitigation efforts: the Polluter- Pays principle (“common but differentiated responsibilities”), suggesting that the countries with the highest greenhouse gas emissions should contribute more, and the Ability-to-Pay principle (“respective capabilities”), suggesting that economically advantaged countries should contribute more. This paper proposes a new burden-sharing method that integrates the Polluter-Pays and Ability-to-Pay principles without resorting to weighted indicators. We provide an algorithmic procedure to implement the method in polynomial time and conduct an axiomatic study to em- phasize the significance of our approach. Finally, we apply our method using worldwide data.

Abstract : For X(t) a two-sided alpha-stable moving average, this paper studies the conditional distribution of future paths given a piece of observed trajectory when the process is far from its central values. Under this framework, vectors of the form (X(t-m),...,X(t),X(t+1),...,X(t+h)), are multivariate alpha-stable and the dependence between the past and future components is encoded in their spectral measures. A new representation of stable random vectors on unit cylinders sets for an adequate semi-norm is proposed to describe the tail behaviour of alpha-stable vectors when only the first m+1 components are assumed to be observed and large in norm.  Not all stable vectors admit such a representation and X(t) will have to be "anticipative enough" to admit one. The conditional distribution of future paths can then be explicitly derived using the regularly varying tails property of stable vectors and has a natural interpretation in terms of pattern identification. Through Monte Carlo simulations we develop procedures to forecast crash probabilities and crash dates and demonstrate their finite sample performances. As an empirical illustration, we estimate probabilities and reversal dates of El Niño and La Niña occurrences.