Estudamos se as intervenções cambiais do Banco Central do Brasil impactam, além da taxa de câmbio, outros preços de ativos (taxas de juros e preços de ações). Fazemos isso classificando as intervenções em três tipos, de acordo com o nível de surpresa, e usando dados minuto a minuto. Nossos resultados mostram que, tanto para a venda de USD (ou emissão de swap) quanto para a compra de USD (ou emissão de swap reverso), o BRL/USD reage na direção esperada, os preços das ações aumentam e as taxas de juros também aumentam. Vale ressaltar que o anúncio impacta muito mais do que a própria intervenção. Além disso, os vértices longos dos juros tendem a responder mais as intervenções do que os vértices curtos. Finalmente, entre os tipos de intervenções, encontramos uma notável heterogeneidade em termos de movimentação dos preços dos ativos dentro de uma janela de meia hora, como em termos de sustentação do movimento por uma janela de nove horas (duração do pregão).

I investigate how motherhood impacts women in the Brazilian labor market. Social norms that regard women’s role as more ”family-oriented,” unequal division of non-market work, and the lack of accessible free childcare for all working mothers could impact their labor supply, wages, and career path. Using an administrative linked employer-employee dataset, I estimate children’s impact on several labor market outcomes through an event-study methodology comparing mothers and non-mothers. While a child’s birth is associated with a decline in the mother’s earnings, participation in the formal labor market, and the probability of holding a managerial position, it is also associated with an increase in participation in the public sector and part-time jobs. In addition, I found that employment penalties are reduced if women are wealthier, college-graduated, and public sector employees. Further, I use household survey data to investigate short-run gender differences in child penalties. I find a decrease in mothers´ wages, employment, and an increase in the probability of holding an informal job after the stability period in the formal labor market. Men do not present changes in labor market outcomes due to parenthood.

In this work, I study the supply and demand for housing in the São Paulo Metropolitan Area, a major city in Brazil. Using detailed commuting data, I estimate a quantitative spatial model, in which agents make decisions on residence and workplace based on local rents, wages, commuting costs, and amenities. I propose an extension of the usual framework with a formal housing supply sector to include a competing informal one, an important institutional characteristic present in many developing countries. I quantify the spatial spillovers of this informal housing, and investigate its role in providing residents with improved access to the local labor market.

Lengthening school time is an attractive policy that middle-income countries have been adopting. However, regardless of an increasing number of works analyzing the impact of increasing instructional time on students’ achievement, little is known about the spillover that this kind of policy generates, despite its high cost for poor students from developing countries. Therefore, we evaluate the impact of full-day schools on regular public schools (part-time). To do so, we analyze the Programa de Ensino Integral (PEI) of São Paulo state for secondary education in a dataset with schools geolocations. We use a dynamic difference-in-differences strategy to show that full-day schools change students and teachers composition of nearby regular schools and negatively affect their achievement and drop-out. Furthermore, we estimate PEI’ impact on treated schools removing spillover bias and confirm that the spillovers are relatively small compared with the program’s gains.

In this paper, we provide evidence that information about hazard mitigation infrastructure in the United States (U.S.) during an indirect exposure to tropical cyclones and the indirect exposure to tropical cyclones per se generate anomalies in returns after considering the 5 Fama-French factors and momentum. We formulate two possible hypotheses to explain these anomalies: local investor and general market hypotheses. Both hypotheses assume that hazard mitigation investments are lower than the ideal. Their difference is based on how investors interpret the hazard mitigation programs. We focus on local investors’ perceptions about them in the local investor hypothesis. More significant investments in these programs mean more local investors will acknowledge them and their flaws. On the other hand, we focus on general investors’ associations between hazard mitigation investment level and disaster risk in the general market hypothesis. In the end, we give some evidence of the local investors’ hypothesis, but we cannot guarantee that this is the only possible explanation. The whole point depends on how much investors know about hazard mitigation programs. Beyond that, we give evidence that an information channel is the probable path in which the anomalies are generated. Thus, in this dissertation, we shed some light on the uncertainty generated by natural disasters that prices assets, a topic that gets more attention in a warming world.

This paper studies the role of aggregators as intermediaries in the online news industry. I propose a model where firms must appeal to consumers with differentiated tastes, trading off between a vertical dimension of quality and a horizontal dimension of relevance. In this context, depending on the relative strength of these forces, the presence of the news aggregator may either increase quality and welfare or decrease quality with an ambiguous effect on welfare. I argue that while the first scenario is more in line with the existing theoretical literature on news aggregators, the second seems to be more strongly supported by the empirical evidence. The impact of aggregators in this second scenario may substantiate concerns over the quality of news provision on the internet.

This paper seeks to understand consumer search patterns and whether information frictions play a role in price dispersion in Brazilian gasoline retail. In our setting, consumers must engage in costly search to gain information about the prices charged by gas stations. Empirically, we divide our analysis into two parts. In the first part, we use a structural model that permits us to estimate points of the distribution of search costs.We estimate the model using price data at the station level for multiple markets in Brazil. In the second part, in two independent analyzes, we investigate the determinants of the proportion of consumers with a low amount of search by OLS and construct an estimate for the average search cost per market by fitting our point estimates into a parametric distribution by NLS. Our findings reveal significant variation in consumer search across markets. Furthermore, our results reveal that most consumers do not compare many prices before buying gasoline. Moreover, our estimates indicate that the number of gas stations in a market, the average distance between gas stations, income, and population are important drivers of the proportion of consumers that search in only one gas station before buying. Finally, the estimated average search cost represents 3% of gasoline prices, a non-negligible proportion. Therefore, the results indicate that information frictions are important to explain price dispersion in Brazilian gasoline retail.

Screen quotas in Brazil have been in effect, in their present form, since 2001. Legislation requires movie theaters to screen Brazilian movies for a number of days on a yearly basis. Even though two decades have passed since its inception, quantitative analyses of the policy’s effects have been scarce. Furthermore, the policy expired by the end of 2021. The reintroduction of quotas is certainly a matter of legislative relevance in the upcoming years. To investigate policy effects, we first run a set of reduced-form regressions, using exogenous variation in the movie theater quotas per viewing room. Next, we build and estimate a dynamic discrete choice model of exhibitor choice. Reduced-form regressions point to negative effects of screen quotas on overall and foreign films’ box-office and ticket sales, but impact on Brazilian movie revenue or public is either zero or very small. Nevertheless, quotas do seem to prompt movie theaters to screen more Brazilian movies.

There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector-autoregressive models with heavy tailed, weakly dependent innovations with virtually no assumption on the conditional heteroskedasticity. In contrast to current literature, our innovation process satisfy an L1 mixingale type condition on the centered conditional covariance matrices. This condition covers L1-NED sequences and strong mixing sequences as particular examples. From a modeling perspective, it covers several multivariate-GARCH specifications, such as the BEKK model, and other factor stochastic volatility specifications that were ruled out by assumption in previous studies.

Motivados pela posição de destaque do real brasileiro entre uma das moedas mais depreciadas em relação aos seus pares emergentes em meados de 2020, potencialmente fomentada pela expressiva evasão de capitais observada no decorrer da pandemia COVID-19 que culminou com um déficit no mercado cambial, realizamos previsões um trimestre a frente para os fluxos de capitais líquidos para o Brasil através de técnicas de machine learning, utilizando modelos de regularização para seleção das variáveis importantes. Os fluxos são obtidos a partir de dados trimestrais do balanço de pagamentos, englobando 2004:T1 a 2021:T1. Os modelos propostos, tanto LASSO quanto adaLASSO + OLS, foram capazes de gerar previsões fora da amostra melhores que o modelo de benchmark, AR. Apesar disso, quando comparados entre si, não podemos rejeitar a hipótese nula de que os modelos propostos possuem a mesma precisão de previsão.

Recently, there has been growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial/synthetic counterfactual from a pool of “untreated” peers, organized in a panel data structure. In this article, we investigate the consequences of applying such methodologies when the data comprise integrated processes of order 1, I(1), or are trend-stationary. We find that for I(1) processes without a cointegrating relationship (spurious case) the estimator of the effects of the intervention diverges, regardless of its existence. Although spurious regression is a well-known concept in time-series econometrics, they have been ignored in most of the literature on counterfactual estimation based on artificial/synthetic controls. For the case when at least one cointegration relationship exists, we have consistent estimators for the intervention effect albeit with a nonstandard distribution. Finally, we discuss a test based on resampling which can be applied when there is at least one cointegration relationship or when the data are trend-stationary.

We estimate a macroeconomic model on US data where banks lend to households and businesses and simultaneously adjust lending requirements on the two types of loans. We find that the collateral shock, a change in the ability of the financial sector to redeploy collateral, is the most important force driving the business cycle. Hit by this unique disturbance, our model quantitatively replicates the joint dynamics of output, consumption, investment, employment, and both household and business credit quantities and spreads. The estimated collateral shock generates accurate movements in lending standards and tracks measures of market sentiment.

Covered Interest Parity deviations (CIP) have been large and persistent among G10 currencies since the global financial crisis in 2008. One of the explanations for the CIP condition breakdown are the new banking regulations that arose in the post-crisis period. On the other hand, CIP deviations for the Brazilian economy have been associated with the EMBI+ index, which is a measure of country risk, as in Garcia and Didier (2003). Building on the recent literature on Covered Interest Parity deviations (i.e, the currency basis) among G10 currencies, I show the recent evolution of the cross-currency basis for the G10 economies, during the 2020 pandemic crisis, and then I study the macroeconomic and regulatory drivers of the Brazilian currency basis. Using the regression approach of Cerutti et al (2021), I find that the FX bid-ask spread has a prominent effect on the real/dollar basis. Using a difference-in-differences approach, I find that the Brazilian currency basis rises at quarter-ends, which is the period when forward contracts appear on banks’ balance sheets. This points to a causal effect of banking regulation on the currency basis, in line with Du, Tepper and Verdelhan (2018).

The paper proposes a quantile-regression inference framework for first-price auctions with symmetric risk-neutral bidders under the independent private-value paradigm. It is first shown that a private-value quantile regression generates a quantile regression for the bids. The private-value quantile regression can be easily estimated from the bid quantile regression and its derivative with respect to the quantile level. This also allows to test for various specification or exogeneity null hypothesis using the observed bids in a simple way. A new local polynomial technique is proposed to estimate the latter over the whole quantile level interval. Plug-in estimation of functionals is also considered, as needed for the expected revenue or the case of CRRA risk-averse bidders, which is amenable to our framework. A quantile-regression analysis to USFS timber is found more appropriate than the homogenized-bid methodology and illustrates the contribution of each explanatory variables to the private-value distribution. Linear interactive sieve extensions are proposed and studied in the Appendices. 

This study exploits non-conventional data sources such as newspaper textual data and internet searches from Google Trends in two empirical problems: (i) analysing the impacts of mobility on cases and deaths due to Covid-19; (ii) nowcasting GDP in high-frequency. The first paper resorts to unstructured data to control for non-observable behavioural effects and finds that an increase in residential mobility significantly reduces Covid-19 cases and deaths over a 4-week horizon. The second paper uses unstructured data sources to nowcast GDP on a weekly basis, showing that textual data and Google Trends can significantly enhance the quality of nowcasts (measured by MSE, MAE and other metrics) compared to Focus’s market expectations as a benchmark. In both cases, unstructured data was revealed to be a valuable source of information not encoded in structured indicators.

This paper provides a game-theoretic model of network formation with a continuous effort choice. Efforts are strategic complements for direct neighbors in the network and display global substitution/competition effects. We show that if the parameter governing local strategic complements is larger than the one governing global strategic substitutes, then all pairwise Nash equilibrium networks are nested split graphs. We also consider the problem of a planner, who can choose effort levels and place links according to a network cost function. Again all socially optimal configurations are such that the network is a nested split graph. However, the socially optimal network may be different from equilibrium networks and efficient effort levels do not coincide with Nash equilibrium effort levels. In the presence of strategic substitutes, Nash equilibrium effort levels may be too high or too low relative to efficient effort levels. The relevant applications are crime networks and R&D collaborations among firms, but also interbank lending and trade.