Bayesian analysis of extreme events with threshold estimation

The aim of this paper is to analyze extremal events using Generalized Pareto Distributions (GPD), considering explicitly the uncertainty about the threshold. Current practice empirically determines this quantity and proceeds by estimating the GPD parameters based on data beyond it, discarding all the information available be10w the threshold. We introduce a mixture model that combines a parametric form for the center and a GPD for the tail of the distributions and uses all observations for inference about the unknown parameters from both distributions, the threshold inc1uded. Prior distribution for the parameters are indirectly obtained through experts quantiles elicitation. Posterior inference is available through Markov Chain Monte Carlo (MCMC) methods. Simulations are carried out in order to analyze the performance of our proposed mode1 under a wide range of scenarios. Those scenarios approximate realistic situations found in the literature. We also apply the proposed model to a real dataset, Nasdaq 100, an index of the financiai market that presents many extreme events. Important issues such as predictive analysis and model selection are considered along with possible modeling extensions.

Ensaios sobre política monetária

Esta tese é composta de três ensaios a respeito de política monetária. O primeiro ensaio aborda o canal em que as crises financeiras aumentam a ineficiência alocativa nos países emergentes. O segundo ensaio trata do grau de não-neutralidade da moeda no Brasil de acordo com o modelo de Golosov e Lucas (2007). O terceiro ensaio estima a inclinação da hazard function da precifi cação para o Brasil pela metodologia de Finite Mixture Model.

DSGE model with banking sector for emerging economies : estimated using Bayesian methodology for Brazil

Economias emergentes sofrem importantes restrições de crédito quando comparadas com economias desenvolvidas, entretanto, modelos estocásticos de equilíbrio geral (DSGE) desenhados para economias emergentes ainda precisam avançar nessa discussão. Nós propomos um modelo DSGE que pretende representar uma economia emergente com setor bancário baseado em Gerali et al. (2010). Nossa contribuição é considerar uma parcela da renda esperada como colateral para empréstimos das famílias. Nós estimamos o modelo proposto para o Brasil utilizando estimação Bayesiana e encontramos que economias que sofrem restrição de colateral por parte das famílias tendem a sentir o impacto de choques monetários mais rapidamente devido a exposição do setor bancário a mudanças no salário esperado.

KittyCat: a cognitive model of structure-form discovery

Cognition is a core subject to understand how humans think and behave. In that sense, it is clear that Cognition is a great ally to Management, as the later deals with people and is very interested in how they behave, think, and make decisions. However, even though Cognition shows great promise as a field, there are still many topics to be explored and learned in this fairly new area. Kemp & Tenembaum (2008) tried to a model graph-structure problem in which, given a dataset, the best underlying structure and form would emerge from said dataset by using bayesian probabilistic inferences. This work is very interesting because it addresses a key cognition problem: learning. According to the authors, analogous insights and discoveries, understanding the relationships of elements and how they are organized, play a very important part in cognitive development. That is, this are very basic phenomena that allow learning. Human beings minds do not function as computer that uses bayesian probabilistic inferences. People seem to think differently. Thus, we present a cognitively inspired method, KittyCat, based on FARG computer models (like Copycat and Numbo), to solve the proposed problem of discovery the underlying structural-form of a dataset.