Transparência na política monetária: teoria, empírico e projeção
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Over the last few years we have seen a huge evolution in the way central banks communicate around the World. Besides providing information to all, transparency allows agents to coordinate and is considered important in the control of inflation expectations. Despite the progress towards greater transparency, there are questions about the extent to which it is really desirable from the social point of view. This thesis analyzes the decision to increase transparency in an incomplete information environment, the empirical effect of transparency on inflation and assesses the predictive capacity of inflation models. The first chapter develops a model for an incomplete information environment. From a stylized game where coordination is desirable from an individual point of view, agents use public and private signals to choose their actions. The process of private information acquisition is endogenous and agents choose how much to invest in the accuracy of private information. In addition to the goal of stabilizing the economy, the monetary authority decides how much to reveal in public information. Public information is a two-edge instrument: it provides additional information and also coordinates expectation, serverving as a focal point for agent’s beliefs. However, by acting in high order belief, public information can over-coordinate agents, enhancing the damage of any errors. This chapter focuses on the following questions: Are there gains in increasing the accuracy of public information? In which situations does greater transparency increase social well-being and in what circumstances is opacity optimal? The results indicate that greater accuracy in public information can increase aggregate well-being, especially when information received privately by the government is not extremely accurate and when the cost of obtaining private information is an important channel. For cases where the information received by the government is extremely accurate, the opacity is optimal. The second chapter empirically analyzes the effects of central bank transparency on the level of inflation. From a panel of 100 countries, we assess whether greater transparency is associated with lower inflation, what types of transparency are most relevant, and especially if the effect of transparency on inflation is different between emerging and developed countries. The estimation faces the omitted variable problem: unobservable characteristics of a particular country can lead to both greater transparency and lower inflation. To try to control this endogeneity, we used three different methodologies: a two-step GMM (S-GMM) dynamic panel estimation developed by Arellano-Bover and Blundell-Bond, a fixed-effect panel and an ordinary least squares estimation. The results show that there is enough evidence that countries with greater transparency have lower inflation. Moreover, the effect of transparency in undeveloped countries is highly significant and negatively correlated with the level of inflation, while the effect for developed countries is smaller and less significant. Increased transparency in emerging countries may be related to regime changes, greater commitment to inflation control and credibility. Thus, greater transparency tends to have a significant impact on inflation in these emerging countries. When analyzing the five types of transparency that compose the index, the monetary policy transparency was the most frequently significant. This type of transparency is associated with timely explanations of monetary policy decisions and signals about the future interest trajectory, while the other sub-indices have more structural and bureaucratic characteristics with long-term effects. The third chapter compares the ability to project consumer inflation in Brazil (IPCA) outside the sample of three methodologies: the MIDAS, an augmented factor VAR (FAVAR) and a nowcast with mixed frequency model and dynamic factors. Over the last few years, several models of inflation projections have been suggested. In addition to the traditional time series models, new approaches allow the use of a large number of variables and the incorporation of samples at different frequencies in the same estimation without over-parameterization. In this chapter, we try to evaluate what kind of methodology is best to predict short-term inflation in Brazil. We estimated the models in four-year windows, with the out-of-sample forecast for the following year and forecast horizon one step ahead and compared the performance of the out-of-sample estimation done by the three models with a naive AR(1) model. The results show that the projections made using MIDAS are much more accurate than those estimated by FAVAR and nowcast, and all three methodologies were superior to AR(1). We noticed that there was a significant worsening of predictive capacity in the years 2015 and 2016 models, especially in the FAVAR and nowcast models. Also, we observed gains in accuracy in the combination of projections: the simple arithmetic mean of the projections has a smaller error than the individual projections performed in each estimation.