Browse the categories to access the content of academic, scientific and opinion publications of the professors and students of the Department of Economics PUC-Rio.

Taylor Rule Estimation by OLS

Journal of Monetary Economics

V 124, P 140-154, 06/12/2021

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.

Carlos Viana de Carvalho, Fernanda Feitosa Nechio, Tiago Santana Tristão.

A Simple Model of Network Formation with Competition Effects (a sair)

Journal of Mathematical Economics


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.

Timo Hiller.

Persistent Monetary Non-neutrality in an Estimated Menu-Cost Model with Partially Costly Information (a sair)

AEJ Macroeconomics


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.

Carlos Viana de Carvalho, Marco Bonomo, Rene Garcia, Vivian Malta Nunes, Rodolfo Dinis Rigato.

Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction (a sair)

Journal of the American Statistical Association.


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

Marcelo Medeiros, Ricardo Masini, Jianqing Fan.

Regularized estimation of high-dimensional vector autoregressions with weakly dependent innovations (a sair)

Journal of Time Series Analysis


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.

Marcelo Medeiros, Eduardo F. Mendes, Ricardo Pereira Masini.

Short-Term Covid-19 Forecast for Latecomers (a sair)

International Journal of Forecasting


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.

Marcelo Medeiros, Alexandre Street, Davi Valladão, Gabriel F. R. Vasconcelos, Eduardo Zilberman.

Jumps in Stock Prices: New Insights from Old Data (a sair)

Journal of Financial Markets


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

Bradley S. Paye, James A. Johnson, Marcelo Medeiros.

Informal Labor and the Efficiency Cost of Social Programs: Evidence from the Brazilian Unemployment Insurance Program

American Economic Journal: Economic Policy

V 13, P 167-206, 28/07/2021

t is widely believed that the presence of a large informal sector increases the efficiency cost of social programs in developing countries. We evaluate such claims for the case of Unemployment Insurance (UI) by combining an optimal UI framework with comprehensive data from Brazil. Using quasi-experimental variation in potential UI duration, we find clear evidence for the usual moral hazard problem that UI reduces incentives to return to a formal job. Yet, the associated efficiency cost is lower than in the U.S., and is lower in labor markets with higher informality within Brazil. This is because formal reemployment rates are lower to begin with where informality is higher, so that a larger share of workers would draw UI benefits absent any moral hazard. In sum, efficiency concerns may actually become more relevant as an economy formalizes.

Gustavo Gonzaga, François Gerard.

Price selection

Journal of Monetary Economics

V 122, P 56-75, 30/06/2021

Price selection is a simple, model-free measure of selection in price setting and its contribution to inflation dynamics. It exploits comovement between inflation and the level from which adjusting prices departed. Prices that increase from lower-than-usual levels tend to push inflation above average. Using detailed micro-level consumer price data for the United Kingdom, the United States, and Canada, we find robust evidence of strong price selection across goods and services. At a disaggregate level, price selection accounts for 37% of inflation variance inflthe United Kingdom, 36% in the United States, and 28% in Canada. Price selection is stronger for goods with less frequent price changes or with larger average price changes. Aggregate price selection is considerably weaker. A multisector sticky-price model accounts well for this evidence and demonstrates a monotone relationship between price selection and monetary non-neutrality.

Revisão em maio de 2021

Carlos Viana de Carvalho, Oleksiy Kryvtsov.

Counterfactual Analysis with Artificial Controls: Inference, High Dimensions and Nonstationarity (a sair)

Journal of the American Statistical Association


Recently, there has been growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a “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 counterfactual from a pool of “untreated” peers, organized in a panel data structure. In this paper, we consider a general framework for counterfactual analysis in high dimensions with potentially non-stationary data and either deterministic and/or stochastic trends, which nests well-established methods, such as the synthetic control. Furthermore, we propose a resampling procedure to test intervention effects that does not rely on post-intervention asymptotics and that can be used even if there is only a single observation after the intervention. A simulation study is provided as well as an empirical application where the effects of price changes on the sales of a product is measured.

Marcelo Medeiros, Ricardo Masini.

Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods

Journal of Business & Economic Statistics

V 39, P 98-119, 15/04/2021

Inflation forecasting is an important but difficult task. Here, we explore advances in machine learning (ML) methods and the availability of new datasets to forecast U.S. inflation. Despite the skepticism in the previous literature, we show that ML models with a large number of covariates are systematically more accurate than the benchmarks. The ML method that deserves more attention is the random forest model, which dominates all other models. Its good performance is due not only to its specific method of variable selection but also the potential nonlinearities between past key macroeconomic variables and inflation. Supplementary materials for this article are available online.

Marcelo Medeiros, Eduardo Zilberman, Gabriel F. R. Vasconcelos, Álvaro Veiga.

From Zero to Hero: Realized Partial (Co)Variances (a sair)

Journal of Econometrics


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.

Tim Bollerslev, Marcelo Medeiros, Andrew J. Patton, Rogier Quaedvlieg.

Balance sheet effects in currency crises: Evidence from Brazil


V 22, N 1, P 19-37, 15/03/2021

In third generation currency crises models, balance sheet losses from currency depreciations propagate the crises into the real sector of the economy. To test these models, we built a firm-level database that allowed us to measure currency mismatches around the 2002 Brazilian currency crisis. We found that between 2001 and 2003, firms with large currency mismatches just before the crisis reduced their investment rates 8.1 percentage points more than other publicly held firms. We also showed that the currency depreciation increased exporters revenue, but those with currency mismatches reduced investments 12.5 percentage points more than other exporters. These estimated reductions in investment are economically very significant, underscoring the importance of negative balance sheet effects in currency crises.

Marcio Magalhães Janot, Márcio Garcia, Walter Novaes.

Semiparametric Quantile Models for Ascending Auctions with Asymmetric Bidders (a sair)

Journal of Business & Economic Statistics


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.

Emmanuel Guerre, Jayeeta Bhattacharya , Nathalie Gimenes.

Quantile regression methods for first-price auctions (a sair)

Journal of Econometrics


Emmanuel Guerre, Nathalie Gimenes.

Sectoral Price Facts in a Sticky-Price Model

American Economic Journal: Macroeconomics

V 13, N 1, P 216-256, 10/01/2021

We develop a multisector sticky-price DSGE model that can endogenously deliver differential responses of prices to aggregate and sectoral shocks. Input-output production linkages and a (standard) monetary policy rule contribute to a slow response of prices to aggregate shocks. In turn, labor market segmentation at the sectoral level induces withinsector strategic substitutability in price-setting decisions, which helps the model deliver a fast response of prices to sector-specific shocks. We estimate the model using aggregate and sectoral price and quantity data for the U.S., and find that it accounts well for a range of sectoral price facts.

Carlos Viana de Carvalho, Jae Won Lee, Woong Yong Park.

Nonparametric identification of an interdependent value model with buyer covariates from first-price auction bids

Journal of Econometrics

V 219, P 1-18, 29/09/2020

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.

Emmanuel Guerre, Nathalie Gimenes.

Counterfactual Analysis and Inference With Nonstationary Data (a sair)

Journal of Business & Economic Statistics


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.

Marcelo Medeiros, Ricardo Masini.

Samaritan Bundles: Inefficient Clustering in NGO Projects

Economic Journal

V 130, P 1541–1582, 06/07/2020

This article provides a theoretical framework to understand the tendency of non-governmental organisations (NGOs) to cluster and the circumstances under which such clustering is socially undesirable. NGOs compete through fundraising for donations and choose issues to focus their projects on. Donors have latent willingness-to-give that may differ across issues, but they need to be ‘awakened' to give. Raising funds focusing on the same issue creates positive informational spillovers across NGOs. Each NGO chooses whether to compete in the same market (clustering) with spillovers, or to face weaker competition under issue specialisation. We show that equilibrium clustering is more likely to occur when the share of multiple-issue donors is relatively large, and when the fundraising technology is sufficiently efficient. Moreover, this situation is socially inefficient when the cost of fundraising takes intermediate values and the motivation for donors’ giving is relatively high. We illustrate the mechanisms of the model with several case studies

G. Aldashev , M. Marini, Thierry Verdier.

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