In this paper we use high-dimensional models, estimated by the Least Absolute Shrinkage and Selection Operator (LASSO), to forecast the Brazilian inflation. The models are compared to  benchmark specifications such as linear autoregressive (AR) and the factor models based on principal components. Our results showed that the LASSO-based specifications have the smallest errors for short-horizon forecasts. However, for long horizons the AR benchmark is the best model with respect to point forecasts. The factor model also produces some good long horizon forecasts in a few cases. We estimated all the models for the two most important Brazilian inflation measures, the IPCA and the IGP-M indexes. The results also showed that there are differences on the selected variables for both measures. Finally, the most important variables selected by the LASSO based models are, in general, related to government debt and money. On the other hand, variables such as unemployment and production were rarely selected by the LASSO.

In this article, we propose a class of logarithmic autoregressive conditional duration (ACD)-type models that accommodates overdispersion, intermittent dynamics, multiple regimes, and asymmetries in financial durations. In particular, our functional coefficient logarithmic autoregressive conditional duration (FC-LACD) model relies on a smooth-transition autoregressive specification. The motivation lies on the fact that the latter yields a universal approximation if one lets the number of regimes grows without bound. After establishing sufficient conditions for strict stationarity, we address model identifiability as well as the asymptotic properties of the quasi-maximum likelihood (QML) estimator for the FC-LACD model with a fixed number of regimes. In addition, we also discuss how to consistently estimate a semiparametric variant of the FC-LACD model that takes the number of regimes to infinity. An empirical illustration indicates that our functional coefficient model is flexible enough to model IBM price durations

We study the impact of emigration on local labor markets, based on the construction of a 1,087km road in northeastern Brazil. The new road has changed population substantially, creating new cities along its path and increasing internal migration flows. We first use a reduced-form approach to estimate the effect of emigration on skill groups (defined by education and experience) - a 10 percentage point increase in the proportion of emigrants raises wages by 5%. Then, using a structural approach, we estimate cross effects among groups - although emigration typically raises wages, complementary effects determine negative impacts in some municipalities.

A large theoretical literature emerged in recent years analyzing the positive and normative effects of capital controls, begging for empirical studies to validate it. No emerging market experimented as actively with controls on capital inflows as Brazil did since late 2009. This paper analyzes the impact of those measures. These policies had some success in segmenting the Brazilian from global financial markets, as measured by the spread between onshore and offshore dollar interest rates, as well as ADR premia relative to the underlying local stocks. The measures adopted from late 2009 to mid-2011 did not translate into significant changes in the exchange rate, suggesting limited success in mitigating exchange rate appreciation. However, the exchange rate strongly depreciates after a tax on the notional amount of derivatives is adopted in mid-2011. The last of the three restrictions studied may have depreciated the Brazilian real in the range from 4 to 10 percent. That strong response may have been driven by complementarities with the previous measures, as well as an unexpected easing in monetary policy.

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. The adaLASSO is a one-step implementation of the family of folded concave penalized least-squares. We assume that both the number of covariates in the model and the number of candidate variables can increase with the sample size (polynomially or geometrically). In other words, we let the number of candidate variables to be larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency) and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. This allows the adaLASSO to be applied to a myriad of applications in empirical finance and macroeconomics. A simulation study shows that the method performs well in very general settings with t-distributed and heteroskedastic errors as well with highly correlated regressors. Finally, we consider an application to forecast monthly US inflation with many predictors. The model estimated by the adaLASSO delivers superior forecasts than traditional benchmark competitors such as autoregressive and factor models.

An English auction is studied in which bidders can acquire information during the bidding process, allowing for heterogeneity both in ex-ante private information and the cost of information acquisition. The best response has a simple characterization where the optimal information acquisition time is unaffected by the other bidders’ strategies. We prove the existence of an equilibrium in a novel way by characterizing it as a fixed point in the space of bid distributions rather than the space of bid functions. Furthermore, we show that when bidders have homogeneous ex-ante private information about valuations: (1) The English auction generates more revenue than the Vickrey auction when the number of bidders is sufficiently large; and (2) the English auction is more efficient than the Vickrey auction when the information acquisition cost are relatively small. We present numerical simulations that show that these effects can be large. Our findings provide an additional explanation for the popularity of the English auction, even in settings where the bidders’ valuations are independent

The demographic transition can affect the equilibrium real interest rate through three channels. An increase in longevity or expectations thereof puts downward pressure on the real interest rate, as agents build up their savings in anticipation of a longer retirement period. A reduction in the population growth rate has two counteracting effects. On the one hand, capital per-worker rises, thus inducing lower real interest rates through a reduction in the marginal product of capital. On the other hand, the decline in population growth eventually leads to a higher dependency ratio (the fraction of retirees to workers). Because retirees save less than workers, this compositional effect lowers the aggregate savings rate and pushes real rates up. We calibrate a tractable life-cycle model to capture salient features of the demographic transition in developed economies, and find that its overall effect is a reduction of the equilibrium interest rate by at least one and a half percentage points between 1990 and 2014. Demographic trends have important implications for the conduct of monetary policy, especially in light of the zero lower bound on nominal interest rates. Other policies can offset the negative effects of the demographic transition on real rates with different degrees of success

In a global games setup with imperfect commitment technology, we show that low targets—the ones close to the optimal inflation under perfect commitment—are unattainable, leading to a trade-off between low and credible targets. Moreover, since noisy public information helps to coordinate expectations around the announced target, our article supports unconventional policy prescriptions. First, weaker countries need to impose higher targets. Second, less transparency helps to make the announced target credible and then reduces the optimally announced target. Results are based on a general central bank loss function encompassing models traditionally used to discuss central bank decisions.

We consider the problem faced by a benevolent government agency that procures in each of  periods an indivisible good from one of  firms. The procurement process is complicated by the superior information possessed by firms about their time-varying production costs and efficiency-enhancing efforts. We fully characterize the optimal dynamic procurement. To reduce firms’ informational rents, the government introduces distortions along two dimensions: when selecting from which firm to procure the good and when providing incentives toward efforts in cost reduction. Both distortions interact in a non-trivial way. Firms that draw lower cost parameters in the first period are favored in the selection process in all later periods, which allows for the provision of more powerful incentives.

Em Unconditional Convergence, Rodrik (2011b), documentou que industrias do setor de manufaturas possuem convergência incondicional da produtividade do trabalho. Neste artigo nós propomos um modelo semi-paramétrico para equações de convergência e mostramos que a velocidade de convergência muda sistematicamente com características específicas dos países estudados. Nós consideramos um modelo flexível de transição suave e com múltiplas variáveis de transição. Nós encontramos evidências que as leis do movimento para o crescimento na produtividade da indústria é diferente nos países

This paper investigates the contribution of agricultural output prices and policies to the reduction in Amazon deforestation in the 2000s. Based on a panel of Amazon municipalities from 2002 through 2009, we first show that deforestation responded to agricultural output prices. After controlling for price effects, we find that conservation policies implemented beginning in 2004 and 2008 significantly contributed to the curbing of deforestation. Counterfactual simulations suggest that conservation policies avoided approximately 73,000 km2 of deforestation, or 56 per cent of total forest clearings that would have occurred from 2005 through 2009 had the policies adopted beginning in 2004 and 2008 not been introduced. This is equivalent to an avoided loss of 2.7 billion tonnes of stored carbon dioxide

For a given frequency of price adjustment, monetary non-neutrality is smaller if older prices are disproportionately more likely to change. Selection for the age of prices provides a complete characterization of price-setting frictions in time-dependent models. Selection for older prices is weaker and non-neutralities are larger if the hazard function of price adjustment is less strongly increasing. Selection is weaker if there is heterogeneity in price stickiness. Finally, selection is weaker if durations of price spells are more variable. In particular, the Taylor (1979) model exhibits maximal selection for older prices, whereas the Calvo (1983) model exhibits no selection.

We study price discovery in the Brazilian Foreign Exchange (FX) markets and indicate which market (spot or futures) adjusts more quickly to the arrival of new information. We find that futures market dominates price discovery since it responds for 66.2% of the variation in the fundamental price shock and for 97.4% of the fundamental price composition, corroborating the result provided in previous studies that, in a unique world example, the exchange rate is formed in the futures market. In a dynamic perspective, the futures market is also more efficient since, when markets are subjected to a shock in the fundamental price, it is faster to recover to equilibrium. By computing price discovery according to calendar semesters, we find evidence of the correlation between price discovery metrics and market factors, such as spot market supply-demand disequilibrium, central bank interventions and institutional investors’ pressure.

Nonlinear time series models, especially those with regime-switching and/or conditionally heteroskedastic errors, have become increasingly popular in the economics and finance literature. However, much of the research has concentrated on the empirical applications of various models, with little theoretical or statistical analysis associated with the structure of the processes or the associated asymptotic theory. In this paper, we derive sufficient conditions for strict stationarity and ergodicity of three different specifications of the first-order smooth transition autoregressions with heteroskedastic errors. This is essential, among other reasons, to establish the conditions under which the traditional LM linearity tests based on Taylor expansions are valid. We also provide sufficient conditions for consistency and asymptotic normality of the Quasi-Maximum Likelihood Estimator for a general nonlinear conditional mean model with first-order GARCH errors.