The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the synthetic control method. In high-dimensional setting, we often use principal component or (weakly) sparse regression to estimate counterfactuals. Do we use enough data information? To better estimate the effects of price changes on the sales in our case study, we propose a general framework on counterfactual analysis for high dimensional dependent data. The framework includes both principal component regression and sparse linear regression as specific cases. It uses both factor and idiosyncratic components as predictors for improved counterfactual analysis, resulting a method called Factor-Adjusted Regularized Method for Treatment (FarmTreat) evaluation. We demonstrate convincingly that using either factors or sparse regression is inadequate for counterfactual analysis in many applications and the case for information gain can be made through the use of idiosyncratic  components. We also develop theory and methods to formally answer the question if common factors are adequate for estimating counterfactuals. Furthermore, we consider a simple resampling approach to conduct inference on the treatment effect as well as bootstrap test to access the relevance of the idiosyncratic components. We apply the proposed method to evaluate the effects of price changes on the sales of a set of products based on a novel large panel of sale data from a major retail chain in Brazil and demonstrate the benefits  of using additional idiosyncratic components in the treatment effect evaluations.

Este trabalho busca quantificar o impacto da Proposta de Emenda Constitucional 95/2016, que limita o crescimento dos gastos federais à inflação, no juro neutro do Brasil. Para isso, aplico duas metodologias para análise e construção do juro neutro contrafactual: o método do Controle Sintético, proposto por Abadie (2003), e o Artificial Counterfactual (ArCo), de Carvalho et al. (2015). Os principais resultados sugerem que o juro neutro do Brasil ao final de 2018 era de 3.9%, em oposição aos estimados pelo Controle Sintético e ArCo, de 10.3% e 12.9%, respectivamente. Estes números apontam favoravelmente à ideia de que reformas fiscais de âmbito estrutural possuem efeito sobre o juro neutro brasileiro.

This paper studies optimal targeting policies, consisting of eliminating (preserving) a set of agents in a network and aimed at minimizing (maximizing) aggregate effort levels. Different from the existing literature, we allow the equilibrium network to adapt after a network intervention and consider targeting of multiple agents. A simple and tractable adjustment process is introduced. We find that allowing the network to adapt may overturn optimal targeting results for a fixed network and that congestion/competition effects are crucial to understanding differences between the two settings

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/congestion 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.

In product markets, there exists substantial dispersion in prices for transactions of physically identical goods, and incumbent sellers sell at higher prices than entrants. This study develops a theory of dynamic pricing that explains these facts as results from the same fundamental friction: Buyers are imperfectly aware of which sellers are operating, and the degree of awareness about a seller is  ndogenous. The equilibrium is unique and efficient, and features randomized pricing strategies where incumbents post higher prices than entrants. If buyers' memory depreciation is low, then the equilibrium of the industry tends to approximate perfectly competitive conditions over time.

We study a unique dataset with comprehensive coverage of daily prices in large multi-product 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 multi-product 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 monetary non-neutrality. Our calibrated model generates as little monetary non-neutrality as in Golosov and Lucas (2007).

This paper introduces a version of the interdependent value model of Milgrom and Weber (1982), where the signals are given by an index gathering signal shifters observed by the econometrician and private ones specific to each bidders. The model primitives are shown to be nonparametrically identified from first-price auction bids under a testable mild rank condition. Identification holds for all possible signal values. This allows to consider a wide range of counterfactuals where this is important, as expected revenue in second-price auction. An estimation procedure is briefly discussed.

Bandit problems are pervasive in various fields of research and are also present in  several practical applications. Examples, including dynamic pricing and assortment and the design of auctions and incentives, permeate a large number of sequential treat- ment experiments. Different applications impose distinct levels of restrictions on viable actions. Some favor diversity of outcomes, while others require harmful actions to be closely monitored or mainly avoided. In this paper, we extend one of the most popular bandit solutions, the original et-greedy heuristics, to high-dimensional contexts. Moreover, we introduce a competing exploration mechanism that counts with searching sets based on order statistics. We view our proposals as alternatives for cases where pluralism is valued or, in the opposite direction, cases where the end-user should carefully tune the range of exploration of new actions. We find reasonable bounds for the cumulative regret of a decaying et-greedy heuristic in both cases, and we provide an upper bound for the initialization phase that implies the regret bounds when order statistics are considered to be at most equal but mostly better than the case when random searching is the sole exploration mechanism. Additionally, we show that endusers have sufficient  exibility to avoid harmful actions since any cardinality for the higher-order statistics can be used to achieve stricter upper bound. In a simulation exercise, we show that the algorithms proposed in this paper outperform simple and adapted counterparts.

Investidores aceitam retornos inferiores em ativos que ofereçam liquidez e segurança, sendo tal desconto conhecido como taxa de conveniência. Este estudo utiliza a diferença entre a taxa fixa do swap de juros e a taxa de juros dos títulos do tesouro como medida da taxa de conveniência. Enquanto a literatura comumente foca na análise do referido prêmio especificamente para os Estados Unidos, construo uma estratégica de swap spread para quatro países: Estados Unidos, Reino Unido, Brasil e Polônia, sendo os dois primeiros desenvolvidos e os demais emergentes. Os resultados mostram que a taxa de conveniência dos países desenvolvidos analisados é, em média, positiva e atinge valores extremos durante crise financeira, em linha com os demais artigos sobre o assunto. Diferentemente, a taxa dos países emergentes é, na maior parte do período, negativa e assume montantes ainda menores em momentos de estresse, comportando-se, portanto, de forma oposta. Empiricamente, essa diferença entre a taxa de conveniência calculada para os Estados Unidos e para o Brasil é relacionada a medidas de liquidez e de risco, levando à compreensão de que em momentos de estresse financeiro, o tesouro americano sustenta uma taxa de conveniência ainda maior do que a do tesouro brasileiro.

This doctoral thesis, is comprised of three chapters. In the first chapter, we explore a discontinuity in eligibility for financial aid for private higher education in Brazil to investigate: (i) if students in the lower end of the ability distribution change their behavior in response to a policy designed to subsidize access to private higher education, and (ii) how students react when they are given the chance to choose between public tuition-free institutions and subsidized access to private higher education. We show that eligibility for financial aid increases students’ likelihood of enrolling and persisting in higher education. We also find that eligible students are less likely to enroll in public tuition-free institutions, with no clear impact on the choice of quality at the program level. In the second chapter, we develop a structural discrete choice model of demand to investigate the determinants of major choice. We use this model and the expansion of a government-funded student credit program in Brazil to evaluate how the availability of credit impacts major choice when — as in Brazil — tuition varies at the major level. We find that tuition and expected labor market returns are the main factors determining students’ choice between different majors.We also find that, when student credit is available, students — especially lower income — are less sensitive to price variations at the major level. In the final chapter, we investigate whether water shortages caused by an extreme climate event impact educational performance. We answer this question exploring the consequences of a water rationing policy that affected some neighborhoods in Brazil’s Distrito Federal. Comparing the academic performance of students enrolled in schools located in neighborhoods affected by the rationing against the performance of students enrolled in non-affected neighborhoods, we find that water rationing has a negative and significant impact on students’ performance. In particular, we show that the impact is significantly stronger for students enrolled in schools with poor infrastructure.