The asymmetric behavior of the U.S public debt

Our main goal in this paper was to measure how e¢ cient is risk sharing between countries. In order to do so, we have used a international risk sharIn this paper we re-analyze the question of the U.S. public debt sustainability by using a quantile autoregression model. This modeling allows for testing whether the behavior of U.S. public debt is asymmetric or not. Our results provide evidence of a band of sustainability. Outside this band, the U.S. public debt is unsustainable. We also nd scal policy to be adequate in the sense that occasional episodes in which the public debt moves out of the band do not pose a threat to long run sustainability.

The asymmetric behavior of the U.S. public debt

In this paper we re-analyze the question of the U.S. public debt sustainability by using a quantile autoregression model. This modeling allows for testing whether the behavior of U.S. public debt is asymmetric or not. Our results provide evidence of a band of sustainability. Outside this band, the U.S. public debt is unsustainable. We also find fiscal policy to be adequate in the sense that occasional episodes in which the public debt moves out of the band do not pose a threat to long run sustainability.

The behavior of temporary sales in a macroeconomic context

Nós investigamos promoções temporárias usando uma base de dados detalhada de 13 anos sobre preços ao consumidor no Brasil, com cotações de preços coletadas decendialmente. Nós encontramos forte evidências da existência de relação entre a frequência e tamanho de promoções e as variáveis macroeconômicas. A crença comum na literatura de que promoções não reagem a mudanças nas variáveis macroeconômicas pode ser devido a baixa volatilidade do cenário macro- econômico nos países analisados até o presente momento.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual horizons. The data to be used consists of metal-commodity prices in a monthly frequency from 1957 to 2012 from the International Financial Statistics of the IMF on individual metal series. We will also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009) , which are available for download. Regarding short- and long-run comovement, we will apply the techniques and the tests proposed in the common-feature literature to build parsimonious VARs, which possibly entail quasi-structural relationships between different commodity prices and/or between a given commodity price and its potential demand determinants. These parsimonious VARs will be later used as forecasting models to be combined to yield metal-commodity prices optimal forecasts. Regarding out-of-sample forecasts, we will use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates to forecast the returns and prices of metal commodities. With the forecasts of a large number of models (N large) and a large number of time periods (T large), we will apply the techniques put forth by the common-feature literature on forecast combinations. The main contribution of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding forecasting, we show that models incorporating (short-run) commoncycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation. Still, in most cases, forecast combination techniques outperform individual models.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual frequencies. Data consists of metal-commodity prices at a monthly and quarterly frequencies from 1957 to 2012, extracted from the IFS, and annual data, provided from 1900-2010 by the U.S. Geological Survey (USGS). We also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009). We investigate short- and long-run comovement by applying the techniques and the tests proposed in the common-feature literature. One of the main contributions of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding out-of-sample forecasts, our main contribution is to show the benefits of forecast-combination techniques, which outperform individual-model forecasts - including the random-walk model. We use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates and functional forms to forecast the returns and prices of metal commodities. Using a large number of models (N large) and a large number of time periods (T large), we apply the techniques put forth by the common-feature literature on forecast combinations. Empirically, we show that models incorporating (short-run) common-cycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation.