Race-neutral affirmative action in higher education has gained importance following the controversies over their race-based alternatives. In many settings, these interventions use a school-based criterion that selects beneficiaries relative to their peers. Exploiting a nationwide quota policy in Brazil that reserved a large share of vacancies in higher education for publicschool students, I show that the reform increases the private-to-public school transitions, especially among students of low-performing private schools. In addition to a direct decrease in returns of the private-school investment, spillovers on indirectly exposed cohorts and general equilibrium effects in the school system might also explain the results

We develop a theory of low-frequency movements in inflation expectations, and use it to interpret joint dynamics of inflation and inflation expectations for the United States and other countries over the post-war period. In our theory long-run inflation expectations are endogenous. They are driven by short-run inflation surprises, in a way that depends on recent forecasting performance and monetary policy. This distinguishes our theory from common explanations of low-frequency properties of inflation. The model, estimated using only inflation and short-term forecasts from professional surveys, accurately predicts observed measures of long-term inflation expectations and identifies episodes of unanchored expectations.

This paper proposes a generalization of the class of realized semivariance and semicovariance measures introduced by Barndorff-Nielsen et al. (2010) and Bollerslev et al. (2020a) to allow for a finer decomposition of realized (co)variances. The new “realized partial (co)variances” allow for multiple thresholds with various locations, rather than the single fixed threshold of zero used in semi (co)variances. We adopt methods from machine learning to choose the thresholds to maximize the out-of-sample forecast performance of time series models based on realized partial (co)variances. We find that in low dimensional settings it is hard, but not impossible, to improve upon the simple fixed threshold of zero. In large dimensions, however, the zero threshold embedded in realized semi covariances emerges as a robust choice.

This paper studies the effects of campaign spending limits on the political entry, selection, and behavior of local politicians in Brazil. We analyze a reform that limits campaign spending for mayoral elections. The limits were implemented with a discontinuity that we exploit for causal identification. We find that stricter limits reduce reelection rates and increase political competition by attracting more candidates who are also less wealthy and rely less on self-financing. Despite their effects on electoral outcomes, stricter limits did not lead to significant short-run improvements in policy outcomes, such as in education and health.

In the absence of strong incentives, public service delivery crucially depends on bureaucrat selection. Despite wide adoption by governments, it is unclear whether civil service examinations reliably select for job performance. We investigate this question focusing on state judges in Brazil. Exploring monthly data on judicial output and cross-court movement, we estimate that judges account for at least 23% of the observed variation in number of cases disposed. With novel data on admission examinations, we show that judges with higher grades perform better than lower-ranked peers. Our results suggest competitive examinations can be an effective way to screen candidates.

We characterize jump dynamics in stock market returns using a novel series of intraday prices covering over 80 years. Jump dynamics vary substantially over time. Trends in jump activity relate to secular shifts in the nature of news. Unscheduled news often involving major wars drives jump activity in early decades, whereas scheduled news and especially news pertaining to monetary policy drives jump activity in recent decades. Jump variation measures forecast excess stock market returns, consistent with theory. Results support models featuring a separate jump factor such that risk premium dynamics are not fully captured by volatility state variables

I analyze how two reforms, introduced to expand college access in Brazil, impacted enrollments of low-SES students. The first policy centralized applications in a nationwide platform (SISU), and the second expanded affirmative action quotas (AA) to a uniform share of 50 percent of vacancies offered by degree. Results show that SISU changes enrollment decisions of high-SES students, crowding out low-SES groups from the least competitive degrees disproportionately. In contrast, AA increases enrollments of low-SES individuals not only mechanically but also through behavioral responses. Finally, their interaction creates a complementary effect, protecting the low-SES groups from the crowding-out of centralization

We study the asymmetric effects of procyclical and countercyclical expansions of public banks’ credit on economic growth. Using a panel of Brazilian municipalities (2009-2014) and the same identification strategy as Greenstone et al. (2020), we show that the effect of public credit on the economic performance of Brazilian municipalities was more substantial in 2009, the only year in our sample in which the public credit policy was countercyclical. Interestingly, our estimates also suggest that the effect of credit policies on growth is more salient when the banking market is more concentrated. Guided by the empirical results we built a theoretical model, which is calibrated with Brazilian data. Policy experiments based on the model demonstrate that countercyclical public credit policies are more effective than procyclical public credit policies. In line with the empirical results, the theoretical model also shows that public credit policies in general (either in booms or in recessions) are more efficient when credit markets are more concentrated. This result indicates that the structure of credit markets is crucial to explain the impact of public credit policies on growth.

The number of Covid-19 cases is increasing dramatically worldwide, with several countries experiencing a second and worse wave. Therefore, the availability of reliable forecasts for the number of cases and deaths in the coming days is of fundamental importance. We propose a simple statistical method for short-term real-time forecasting of the number of Covid-19 cases and fatalities in countries that are latecomers  i.e., countries where cases of the disease started to appear some time after others. In particular, we propose a penalized (LASSO) regression with an error correction mechanism to construct a model of a latecomer in terms of the other countries that were at a similar stage of the pandemic some days before. By tracking the number of cases in those countries, we forecast through an adaptive rolling-window scheme the number of cases and deaths in the latecomer. We apply this methodology to four dierent countries: Brazil, Chile, Mexico, and Portugal. We show that the methodology performs very well. These forecasts aim to foster a better short-run management of the health system capacity and can be applied not only to countries but to dierent regions within a country, as well.

In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based  methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly  reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data

We develop a two-period overlapping generations model in which both the family structure and the decision to commit crime are endogenous and the dynamics of moral norms of good conduct is transmitted intergenerationally by families and peers. By "destroying" biparental families and putting fathers in prison, we show that more intense crime repression can backfire because it increases the possibility that criminals' sons become criminals themselves. Our model also explains the emergence and persistence of urban ghettos characterized by a large proportion of broken families, high crime rates, and high levels of peer socialization, which reinforce criminal activities

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.

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.

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 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.

The article proposes a parsimonious and flexible semiparametric quantile regression specification for asymmetric bidders within the independent private value framework. Asymmetry is parameterized using powers of a parent private value distribution, which is generated by a quantile regression specification. As noted in Cantillon, this covers and extends models used for efficient collusion, joint bidding and mergers among homogeneous bidders. The specification can be estimated for ascending auctions using the winning bids and the winner’s identity. The estimation is in two stage. The asymmetry parameters are estimated from the winner’s identity using a simple maximum likelihood procedure. The parent quantile regression specification can be estimated using simple modifications of Gimenes. Specification testing procedures are also considered. A timber application reveals that weaker bidders have 30% less chances to win the auction than stronger ones. It is also found that increasing participation in an asymmetric ascending auction may not be as beneficial as using an optimal reserve price as would have been expected from a result of Bulow and Klemperer valid under symmetry.

Using rich administrative and household survey data spanning 34 years from 1985 to 2018, we document a series of new facts on earnings inequality and dynamics in a developing country with a large informal sector: Brazil. Since the mid-1990s, both inequality and volatility of earnings have declined significantly in Brazil’s formal sector. Higher-order moments of the distribution of earnings changes show cyclical movements in Brazil that are similar to those in developed countries like the US. Relative to the formal sector, the informal sector is associated with a significant earnings penalty and higher earnings volatility for identical workers. Earnings changes of workers who switch from formal to informal (from informal to formal) employment are relatively negative (positive) and large in magnitude, dispersed, negatively (positively) skewed, and less leptokurtic. Our results suggest that informal employment is an imperfect insurance mechanism

This paper adds to the scant literature on the impact of terrorism on consumer behaviour, focusing on household spending on goods that are sensitive to brain-stress neurocircuitry. These include sweet- and fat-rich foods but also home necessities and female-personal-hygiene products, the only female-targeted good in our data. We examine unique continuous in-home-scanner expenditure data for a representative sample of about 15,000 French households, observed in the days before and after the terrorist attack at the Bataclan concert-hall. We find that the attack increased expenditure on sugar-rich food by over 5% but not that on salty food or soda drinks. Spending on home maintenance products went up by almost 9%. We detect an increase of 23.5% in expenditure on women’s personal hygiene products. We conclude that these effects are short-lived and driven by the responses of households with children, youths, and those residing within a few-hours ride of the place of the attack.

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 inflation and the output gap, that are endogenous to monetary policy shocks. Endogeneity implies a correlation between regressors and the error term – hence, an asymptotic bias. In principle, Instrumental Variables (IV) estimation can solve this endogeneity problem. In practice, however, 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 due to monetary policy shocks. Using Monte Carlo simulations, we then show that this relationship also holds in a quantitative model of the U.S. economy. Since monetary policy shocks explain only a small fraction of the variance of regressors typically included in monetary policy rules, the endogeneity bias tends to be small. For realistic sample sizes, OLS outperforms IV. Finally, we estimate a standard Taylor rule on different subsamples of U.S. data and find that OLS and IV estimates are quite similar.