A partir de meados dos anos 90 diversas reformas institucionais e revisões normativas e legais ocorridas no Brasil provocaram uma onda de fusões e aquisições (F&As) bancárias. Como resultado de tal processo, o nível de concentração bancária tornou-se hoje no Brasil um dos maiores do mundo. O objetivo desta pesquisa é tentar evidenciar os efeitos da concorrência bancária sobre o crédito no Brasil através do estudo de dados de três dos principais eventos de F&A que ocorreram após o Plano Real. Para estimar o efeito da competição nas variáveis de crédito, foi utilizado o modelo de diferenças em diferenças (DD), desenvolvido no trabalho de Joaquim, Doornik e Ornelas (2019). Sob a hipótese de identificação de que na ausência de um episódio de F&A em um mercado bancário local, os resultados entre municípios expostos e não expostos serão semelhantes, foram considerados mercados tratados aqueles com pelo menos uma agência de ambos os bancos envolvidos no episódio de F&A. Ao replicar os resultados do modelo DD em questão para o caso do Itaú-Unibanco, utilizando apenas dados públicos, e realizar o estudo de caso dos episódios de F&A entre o Santander e ABN AMRO Real (2007) e Bradesco e HSBC (2016), foi evidenciado que uma redução na competição bancária através de bancos privados implica em uma redução no volume de empréstimos de bancos privados e que a trajetória do crédito privado tem pouco efeito sobre os rivais de propriedade pública. Além disso, foi observado que a presença geográfica dos bancos, ou número de agências espalhadas pelas regiões do Brasil não interfere nesses resultados.

O presente estudo busca analisar o comportamento do investidor brasileiro em diferentes ambientes de taxas de juros e compara esse comportamento com a teoria tradicional de finanças e as finanças comportamentais. Além de dados quantitativos disponíveis do mercado de capitais brasileiro, o estudo desenvolve uma pesquisa qualitativa, observacional de campo – de autoria dos autores. A pesquisa de campo busca adaptar para o mercado brasileiro o trabalho de Yoav Ganzach and Avi Wohl (2018) “A Behavioral Theory of the Effect of the Risk-Free Rate on the Demand for Risky Assets” que sugere que taxas de juros mais baixas mudam a propensão a tomar risco por parte dos investidores. Esse tipo de abordagem vai de encontro ao que sugere a microeconomia clássica, segundo a qual, o que importa é o prêmio de risco, ou o retorno superior ao retorno sem risco. Dito de outra forma, segundo a literatura clássica, o nível de retorno livre de risco não importa. O presente trabalho reproduz experimentos no mercado brasileiro visando compreender se prêmio de risco é uma variável relevante ou se o nível de juros altera a forma de se investir. Além disso o trabalho busca entender se há distinção entre investidores de varejo e de alta renda.

Central-government politicians channel resources to sub-national entities for political gains. We show formally that the central politicians' allocation decision has two drivers: political alignment (between central and local politicians) and the level of local political accountability. However, drivers count one at a time: alignment matters before local elections, while local political accountability matters before central elections. We then perform a test of our model using Brazilian data, which corroborates our results. Furthermore, we show and explain why political accountability becomes a curse: better-educated districts receive fewer transfers in equilibrium.

Modelos de previsão cambial são frequentemente preteridos em relação a passeios aleatórios, porém o trabalho mostra que em certos casos, principalmente à médio e longo prazo, modelos simples de previsão cambial podem ser melhores do que passeio aleatório em países emergentes com câmbio livre. O trabalho também mostra que não há uma reversão do câmbio real à sua média de longo prazo e que seu movimento após um choque é feito quase todo pelo câmbio nominal.

We adopt an artificial counterfactual approach to assess the impact of lockdowns on the short-run evolution of the number of cases and deaths in some US states. To do so, we explore the different timing in which US states adopted lockdown policies, and divide them among treated and control groups. For each treated state, we construct an artificial counterfactual. On average, and in the very short-run, the counterfactual accumulated number of cases would be two times larger if lockdown policies were not implemented

We propose a model that reconciles microeconomic evidence of frequent and large price changes with sizable monetary non-neutrality. Firms incur separate lump-sum costs to change prices and to gather and process some information about marginal costs. Additional relevant information is continuously available, and can be factored into pricing decisions at no cost. We estimate the model by Simulated Method of Moments, using price-setting statistics for the U.S. economy. The model with free idiosyncratic and costly aggregate information fits well both targeted and untargeted microeconomic moments and generates more than twice as much monetary non-neutrality as the Calvo model.

Ordinary Least Squares (OLS) estimation of monetary policy rules produces potentially inconsistent estimates of policy parameters. The reason is that central banks react to variables, such as in ation and the output gap, which are endogenous to monetary policy shocks. Endogeneity implies a correlation between regressors and the error term, and hence, an asymptotic bias. In principle, Instrumental Variables (IV) estimation can solve this endogeneity problem. In practice, IV estimation poses challenges, as the validity of potential instruments depends on various unobserved features of the economic environment. We argue in favor of OLS estimation of monetary policy rules. To that end, we show analytically in the three-equation New Keynesian model that the asymptotic OLS bias is proportional to the fraction of the variance of regressors accounted for by monetary policy shocks. Using Monte Carlo simulation, we then show that this relationship also holds in a quantitative model of the U.S. economy. As monetary policy shocks explain only a small fraction of the variance of regressors typically included in monetary policy rules, the endogeneity bias is small. Using simulations, we show that, for realistic sample sizes, the OLS estimator of monetary policy parameters outperforms IV estimators.

A sair no Journal of Monetary Economics

Insurance is a possible explanation for the large holdings of international reserves observed in many countries. Quantitative models of the insurance motive, however, struggle to rationalize reserve positions, unless agents exhibit relatively high levels of risk aversion. This result suggests a connection between the "international reserves puzzle" and the "equity premium puzzle", which I explore in this paper. I introduce Epstein-Zin preferences into a standard sovereign default model with long-term debt and a risk-free asset, and calibrated it to the Mexican economy. I then price an equity claim within the model, and use simulations to establish a positive relationship between optimal reserve holdings and the equity premium, as I vary the degree of risk aversion of domestic agents. Using an estimate of the equity premium for Mexico, I calibrate the level of risk aversion and find it produces an optimal level of international reserves that is close to the data. Finally, I provide empirical evidence consistent with the relationship established with the model. Specifically, I introduce estimates of the equity premium into standard regressions used to explain countries' holdings of international reserves. Using both cross-sectional and panel specifications, I document a robust positive association between these two variables.

This paper examines the potential explanations for the Missing Disinflation Puzzle (MDP). We construct a data set containing only variables associated with the puzzle, and use of Machine Learning (ML) methods to compute estimates for U.S. Consumer Price Index inflation over the period of interest. These methods can handle large data sets, and perform variable selection. A model selection exercise using Model Confidence Set over pseudo-out-of-sample forecasts is proposed to assess forecasting performance and to analyze the variable selection pattern of these models. We analyze the variable selection performed by the best models and find evidence for explanations associated with different metrics for inflation expectations - in particular those linked to consumers’ surveys.   

This paper aims to decompose the banking spread using a structural macroeconomic model. We embedded a general equilibrium framework with loans to individuals and firms that may be in default, a banking sector in monopolistic competition and subject to administrative costs, and we also added a tax structure related to bank intermediation. These characteristics for the composition of the spread are in line with the empirical literature on banking spread determinants in Brazil and with the accounting decomposition of the spread made by the Banco Central do Brasil. Furthermore, we evaluate the model dynamics responding to shocks.