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The effect of imposing different numbers of unit roots on forecasting accuracy is examined using univariate ARMA models. To see whether additional information improves forecasting accuracy and increases the informative forecast horizon, the authors cross-relate the time series for inbound tourism in Sweden for different visitor categories and estimate vector ARMA models. The mean-squared forecast error for different filters indicates that models in which unit roots are imposed at all frequencies have the smallest forecast errors. The results from the vector ARMA models with all roots imposed indicate that the informative forecast horizon is greater than for the univariate models. Out-of-sample evaluations indicate, however, that the univariate modelling approach may be preferable.

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Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This article presents a new, more efficient algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the article, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.

This paper attempts to evaluate the role of technology in combination with resource endowments and economies of scale as determinants of industrial patterns of comparative advantage, international competitiveness and specialization within manufacturing among OECD countries. Thus we attempt to combine two paradigms from trade theory, namely the technology or Ricardian view, and the factor proportions or Heckscher-Ohlin explanations of changes in trade patterns.Within the large empirical literature on the determinants of patterns of comparative advantage and specialization (for surveys see Deardorff 1984 andLeamer 1994), most studies treat the role of factor endowments. Technology has been introduced into the empirical analysis of comparative advantage in various ways. Early studies used relative labor productivity data (MacDougall 1951(MacDougall , 1952 to explain countries' specialization.Other studies found R&D intensity, in addition to a set of factor proportions variables, to be positively related to US export performance (Gruber, Metha & Vernon 1967, Stern & Maskus 1981. Variables like product age or income elasticity have been used (Wells 1969, Hufbauer 1970, Finger 1975 to proxy various aspects of technology.Introducing R&D intensity as a product characteristic, as in these studies, implies that R&D capacity is treated as just another resource. A more satisfactory approach, based on Posner's (1961) concept of technology gaps, is to explain competitiveness in terms of UHODWLYH R&D intensity, where high values are assumed to result in better products and/or more efficient methods. On the macro level, differences in national R&D activity has been shown to influence export growth, i.e. DEVROXWH advantage, more than traditional measures of price competitiveness (Fagerberg 1988).There is a growing literature on the role of technology for FRPSDUDWLYH advantage or UHODWLYH international competitiveness, measured on the industry level by (gross) exports, export shares, revealed comparative advantage or net export shares of consumption (for a survey see Verspagen & Wakelin 1996). These studies use different proxies for technology. While R&D expenditure measures the input of resources in the