3 resultados para BAYESIAN-ESTIMATION

em Repositório digital da Fundação Getúlio Vargas - FGV


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Esta tese tem como objetivo principal aproximar a evidencia empirica existente sobre os agregados macroeconomicos com as novas evidencias empiricas baseadas nos micro dados de precos ao consumidor, tendo como base os modelos padroes de rigidez de preco utilizados na literatura de politica monetaria. Para isso, esta tese utiliza a base de dados individuais de precos ao consumidor no Brasil fornecida pela Fundacao Getulio Vargas. Especificamente, esta tese foca em tres temas principais: a existencia de variac˜oes temporararias de precos, a heterogeneidade na rigidez de precos entre firmas de um mesmo setor e o formato das func˜oes hazard. Os resultados mostram que: existe de fato uma correlac˜ao entre as variaveis referentes as mudancas temporararias de precos e os agregados macroeconomicos; a heterogeneidade na rigidez de precos entre firmas de um mesmo setor apresenta efeitos significativos sobre a dinamica dos agregados macroeconomicos; e por fim, o formato mais geral da func˜ao hazard proposta nesta tese possibilita novas dinamicas dos agregados macroeconomicos.

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The onset of the financial crisis in 2008 and the European sovereign crisis in 2010 renewed the interest of macroeconomists on the role played by credit in business cycle fluctuations. The purpose of the present work is to present empirical evidence on the monetary policy transmission mechanism in Brazil with a special eye on the role played by the credit channel, using different econometric techniques. It is comprised by three articles. The first one presents a review of the literature of financial frictions, with a focus on the overlaps between credit activity and the monetary policy. It highlights how the sharp disruptions in the financial markets spurred central banks in developed and emerging nations to deploy of a broad set of non conventional tools to overcome the damage on financial intermediation. A chapter is dedicated to the challenge face by the policymaking in emerging markets and Brazil in particular in the highly integrated global capital market. This second article investigates the implications of the credit channel of the monetary policy transmission mechanism in the case of Brazil, using a structural FAVAR (SFAVAR) approach. The term “structural” comes from the estimation strategy, which generates factors that have a clear economic interpretation. The results show that unexpected shocks in the proxies for the external finance premium and the credit volume produce large and persistent fluctuations in inflation and economic activity – accounting for more than 30% of the error forecast variance of the latter in a three-year horizon. Counterfactual simulations demonstrate that the credit channel amplified the economic contraction in Brazil during the acute phase of the global financial crisis in the last quarter of 2008, thus gave an important impulse to the recovery period that followed. In the third articles, I make use of Bayesian estimation of a classical neo-Keynesian DSGE model, incorporating the financial accelerator channel developed by Bernanke, Gertler and Gilchrist (1999). The results present evidences in line to those already seen in the previous article: disturbances on the external finance premium – represented here by credit spreads – trigger significant responses on the aggregate demand and inflation and monetary policy shocks are amplified by the financial accelerator mechanism. Keywords: Macroeconomics, Monetary Policy, Credit Channel, Financial Accelerator, FAVAR, DSGE, Bayesian Econometrics

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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.