This paper sets out a data-driven approach for targeting environmental policies optimally in order to combat deforestation. We focus on the Amazon, the world's most extensive rainforest, where Brazil's federal government issued a `Priority List' of municipalities in 2008 { a blacklist to be targeted with more intense environmental monitoring and enforcement. First, we estimate the causal impact of the Priority List on deforestation (along with other relevant treatment effects) using `changes-in-changes' (Athey and Imbens, 2006), finding that it reduced deforestation by 43 percent and cut emissions by 49 million tons of carbon. Second, we develop a novel framework for computing targeted optimal blacklists that draws on our treatment effect estimates, assigning municipalities to a counterfactual list that minimizes total deforestation subject to realistic resource constraints. We show that the ex-post optimal list would result in carbon emissions

We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe Ratio estimators when there are many assets in a portfolio. We allow for an increasing number of assets as well as time observations of the portfolio. Since the nodewise regression is not feasible due to the unknown nature of idiosyncratic errors, we provide a feasible-residual-based nodewise regression to estimate the precision matrix of errors which is consistent even when number of assets, p, exceeds the time span of the portfolio, n. In another new development, we also show that the precision matrix of returns can be estimated consistently, even with an increasing number of factors andp>n. We show that: (1) withp>n, the Sharpe Ratio estimators are consistent in global minimum-variance and mean–variance portfolios; and (2) withp>n, the maximum Sharpe Ratio estimator is consistent when the portfolio weights sum to one; and (3) withp<

European macroeconomic and financial aggregates move in lockstep over the business cycle. We develop a model in which a single risk premium shock triggers these comovements. The key feature is a financial sector where traditional banks transfer part of their risky loan portfolio to nonbank institutions. We fit the model to euro area data and find that risk premium shocks are the main driver of business and financial cycles over the past two decades.

We study a unique dataset with comprehensive coverage of daily prices in large multiproduct retailers in Israel. Retail stores synchronize price changes around occasional “peak” days when they reprice around 10% of their products. To assess aggregate implications of partial price synchronization, we develop a new model in which multiproduct firms face economies of scope in price adjustment, and synchronization is endogenous. Synchronization of price changes attenuates the average price response to monetary shocks, but only high degrees of synchronization can substantially strengthen the real effects of monetary policy shocks. Our calibrated model generates real effects similar in magnitude to those in Golosov and Lucas (2007).

This paper proposes a novel instrumental variable to estimate the causal impact of law enforcement on deforestation. DETER, a satellite-based system for mapping land cover, is the key tool in Amazon monitoring. It determines the location of recent forest clearings and is used to target enforcement. Because DETER cannot detect land cover patterns beneath clouds, it detects no clearings in covered areas. Results conrm that DETER cloud coverage is systematically correlated with nes, a proxy for the presence of law enforcers. The study explores this exogenous source of variation in the allocation of law enforcers as an instrument for the intensity of enforcement. Stricter law enforcement eectively deterred Amazon deforestation, helping avoid over 22,000 km2 of cleared forest area per sample year. Leakage of criminal activity into neighboring areas does not appear to have occurred. Results are submitted to a series of robustness checks.

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.

We augment a standard dynamic general equilibrium model with financial frictions, in order to quantify the macroeconomic effects of the credit deepening process observed in Latin America in the 2000s—most notably in Brazil. In the model, a stylized banking sector intermediates credit from patient households to impatient households and entrepreneurs. Motivated by the Brazilian experience, we allow the credit constraint faced by households to depend on labor income. Our model is designed to isolate the effects of credit deepening through demand-side channels, and abstracts from potential effects of credit supply on total factor productivity. In the calibrated model, credit deepening generates only modest above-trend growth in consumption, investment, and GDP. Since Brazil has experienced one of the most intense credit deepening processes in Latin America, we argue the quantitative effects that hinge on the channels captured by the model are unlikely to be sizable elsewhere in Latin America.

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

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.

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.

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

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.