Acumulação tecnológica e aprimoramento de performance competitiva: evidências de diferenças e similaridades e médias empresas do setor metal-mecânico no estado do rio de janeiro

The main objective of this dissertation is to examine the implications of technological capacities in the improvement of technical performance indexes, specifically at the company level. These relationships were examined in a small sample of metal-working enterprises in the state of Rio de Janeiro (1960 to 2006). Although diverse studies on technological competences have been carried out in the last twenty years, a gap in empirical studies still exist that correlate the performance of companies in the context of developing countries, especially in Brazil. Aiming to contribute to a reduction of these gaps, this dissertation examines the questions by the light of available models in literature, which opting themselves to using operational indexes of companies. For drawing the accumulation of technological competences in this study, the metric proposal by Figueiredo (2000) shall be used indicating the levels of technological qualification in process, product, and equipment functions. The empirical evidence examined in this dissertation is both qualitative and quantitative in nature and were collected, first hand, through extensive field research involving informal interviews, meetings, direct-site observation and document analysis. In relation to the results, the evidence suggests that: - In terms of technological accumulation, a company reached Level 5 of technological capacity in process and organization of production as well as product and equipment. Three companies obtained Level 4 in the function process function while two others had reached the same technological level in the functions of product and equipment. Two companies had reached Level 3 in the product and equipment functions and one remained this level in the function of process; - In terms of the rate of accumulation of technological capacities, the observed companies had reached Level 4 needs 29 years in process function, 32 years in product function and 29 years in equipment function; - In terms of improvement performance pointers, a company which reached Level 5 of technological capacity improved in 70% of its indicators of performance, while the company that had achieved Level 4 had raised its pointers 60% and the other companies had gotten improved in the order of 40%. It was evidenced that the majority of the pointers of the companies with higher levels of technological capacities had obtained better performance. This dissertation contributes to advancing the strategic management of companies in metal-working segment to understanding internal accumulation of technological capacity and indicators of performance especially in the field of empirical context studied. This information offers management examples of how to improve competitive performance through the accumulation of technological capacities in the process, product and equipment functions.

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.