Essays on the general equilibrium effects of barriers to trade on economic growth, foreign trade and the location of economic activity in Brazil

This work presents a fully operational interstate CGE model implemented for the Brazilian economy that tries to quantify both the role of barriers to trade on economic growth and foreign trade performance and how the distribution of the economic activity may change as the country opens up to foreign trade. Among the distinctive features embedded in the model, modeling of external scale economies, port efficiency and land-maritime transport costs provides an innovative way of dealing explicitly with theoretical issues related to integrated regional systems. In order to illustrate the role played by the quality of infrastructure and geography on the country‟s foreign and interregional trade performance, a set of simulations is presented where barriers to trade are significantly reduced. The relative importance of trade policy, port efficiency and land-maritime transport costs for the country trade relations and regional growth is then detailed and quantified, considering both short run as well as long run scenarios. A final set of simulations shed some light on the effects of liberal trade policies on regional inequality, where the manufacturing sector in the state of São Paulo, taken as the core of industrial activity in the country, is subjected to different levels of external economies of scale. Short-run core-periphery effects are then traced out suggesting the prevalence of agglomeration forces over diversion forces could rather exacerbate regional inequality as import barriers are removed up to a certain level. Further removals can reverse this balance in favor of diversion forces, implying de-concentration of economic activity. In the long run, factor mobility allows a better characterization of the balance between agglomeration and diversion forces among regions. Regional dispersion effects are then clearly traced-out, suggesting horizontal liberal trade policies to benefit both the poorest regions in the country as well as the state of São Paulo. This long run dispersion pattern, on one hand seems to unravel the fragility of simple theoretical results from recent New Economic Geography models, once they get confronted with more complex spatially heterogeneous (real) systems. On the other hand, it seems to capture the literature‟s main insight: the possible role of horizontal liberal trade policies as diversion forces leading to a more homogeneous pattern of interregional economic growth.

Estimating sectoral cycles using cointegration and common features

This paper investigates the degree of short run and long run co-movement in U.S. sectoral output data by estimating sectoraI trends and cycles. A theoretical model based on Long and Plosser (1983) is used to derive a reduced form for sectoral output from first principles. Cointegration and common features (cycles) tests are performed; sectoral output data seem to share a relatively high number of common trends and a relatively low number of common cycles. A special trend-cycle decomposition of the data set is performed and the results indicate a very similar cyclical behavior across sectors and a very different behavior for trends. Indeed. sectors cyclical components appear as one. In a variance decomposition analysis, prominent sectors such as Manufacturing and Wholesale/Retail Trade exhibit relatively important transitory shocks.

A qualidade no serviço de advocacia empresarial: um estudo de caso

° objetivo deste estudo exploratório foi o de investigar a percepção da qualidade no serviço de advocacia empresarial. Para tanto foi realizado um estudo de caso, envolvendo uma organização de grande porte e uma sociedade de advogados que lhe presta serviços de assessoria jurídica, ambos com sede no estado do Rio de Janeiro. Os dados foram obtidos a partir de entrevistas em profundidade com profissionais de ambos os lados, tendo em vista o forte caráter de interação inerente ao serviço estudado e, também, buscando-se identificar eventuais discrepâncias relativas aos critérios de julgamento quanto à sua qualidade. Os resultados indicam que o processo de avaliação da qualidade de serviços tende a apresentar traços distintos na sociedade brasileira devido às peculiaridades de seu sistema cultural. Especificamente quanto ao serviço de advocacia empresarial, restou claro que as expectativas do cliente desempenham um papel relevante para a percepção de sua qualidade, e que fatores não técnicos, ou de natureza funcional, também possuem grande influência no processo.

The importance of common cyclical features in VAR analysis: a Monte-Carlo study

Despite the commonly held belief that aggregate data display short-run comovement, there has been little discussion about the econometric consequences of this feature of the data. We use exhaustive Monte-Carlo simulations to investigate the importance of restrictions implied by common-cyclical features for estimates and forecasts based on vector autoregressive models. First, we show that the ìbestî empirical model developed without common cycle restrictions need not nest the ìbestî model developed with those restrictions. This is due to possible differences in the lag-lengths chosen by model selection criteria for the two alternative models. Second, we show that the costs of ignoring common cyclical features in vector autoregressive modelling can be high, both in terms of forecast accuracy and efficient estimation of variance decomposition coefficients. Third, we find that the Hannan-Quinn criterion performs best among model selection criteria in simultaneously selecting the lag-length and rank of vector autoregressions.

The importance of Common-Cyclical Features in VAR analysis: a Monte-Carlo study (Preliminary Version)

Despite the belief, supported byrecentapplied research, thataggregate datadisplay short-run comovement, there has been little discussion about the econometric consequences ofthese data “features.” W e use exhaustive M onte-Carlo simulations toinvestigate theimportance ofrestrictions implied by common-cyclicalfeatures for estimates and forecasts based on vectorautoregressive and errorcorrection models. First, weshowthatthe“best” empiricalmodeldevelopedwithoutcommoncycles restrictions neednotnestthe“best” modeldevelopedwiththoserestrictions, duetothe use ofinformation criteria forchoosingthe lagorderofthe twoalternative models. Second, weshowthatthecosts ofignoringcommon-cyclicalfeatures inV A R analysis may be high in terms offorecastingaccuracy and e¢ciency ofestimates ofvariance decomposition coe¢cients. A lthough these costs are more pronounced when the lag orderofV A R modelsareknown, theyarealsonon-trivialwhenitis selectedusingthe conventionaltoolsavailabletoappliedresearchers. T hird, we…ndthatifthedatahave common-cyclicalfeatures andtheresearcherwants touseaninformationcriterium to selectthelaglength, theH annan-Q uinn criterium is themostappropriate, sincethe A kaike and theSchwarz criteriahave atendency toover- and under-predictthe lag lengthrespectivelyinoursimulations.

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.