General circulation models (GCM) show projections of climate variables that when downscaled can be applied to analyse future behaviour in different areas or places. Using them is possible not just to obtain expected values of climate variables but also to calculate their distributions and use those values to assess the effects of climate change at a local level. However, these calculations depend on the GCM selected. In this paper, daily maximum near-surface air temperatures from 21 climate models under representative concentration pathway (RCP) scenarios RCP 4.5 and RCP 8.5 and historic daily maximum temperatures (1990–2019) from nine cities in southern Spain are used with two objectives: first, to investigate past behaviour broken down into a deterministic part and a stochastic part; second, to compare historical data (2006–2019) with the information extracted from the 21 GCMs based on calculating goodness of fit in the period for both deterministic and stochastic parts. The methodology proposed may be useful in selecting a model or a range of models for use in a specific study. The results show positive historical and future trends in maximum daily temperature for these cities. The GCMs with the best fit for each city in this specific case are also presented.

Since the reign of Juan II and especially Queen Isabel I of Castile, we have found that silk fabrics had displaced expensive dyed wool cloths from the first place, at least among the privileged groups. At the same time, a very fine linen fabric, the holanda spread in a spectacular way, at least in the case of the House of Isabel I, especially as body linen and household line. In this article, we discuss how these changes could be transmitted downwards through some examples of different social groups - nobility, townspeople, peasants - considering the economic and social limitations that would have been relevant in its diffusion.

Thanks to the conservation of the expense accounting of the Royal House of Isabel I of Castile (1492-1504), it is possible to analyze the consumption of silk, wool and linen fabrics (excluding fabric with metal threads) by class status from the time of the discovery of America to the death of the queen. The most frequently used fabrics by quantity was linen, followed by wool and silk. By cost, however, silk reached 60 % of expenses, followed by wool (31 %) and linen (8 %). As the accounts move down the social ladder, silk disappears altogether, while wool and linen remain. Attending to the colour, black was the most commonly used dye for silk fabrics and red was the most common dye for wool.

The understanding of emerging technologies and the analysis of their development pose a great challenge for decision makers, as being able to assess and forecast technological change enables them to make the most of it. There is a whole field of research focused on this area, called technology forecasting, in which bibliometrics plays an important role. Within that framework, this paper presents a forecasting approach focused on a specific field of technology forecasting: research activity related to an emerging technology. This approach is based on four research fields--bibliometrics, text mining, time series modelling and time series forecasting--and is structured in five interlinked steps that generate a continuous flow of information. The main milestone is the generation of time series that measure the level of research activity and can be used for forecasting. The usefulness of this approach is shown by applying it to an emerging technology: cloud computing. The results enable the technology to be structured into five main sub-technologies which are characterised through five time series. Time series analysis of the trends related to each sub-technology shows that Privacy and Security has been the most active sub-technology to date in this area and is expected to maintain its level of interest in the near future.

Cultural re-imaging through iconic art museums aims to create symbolic capital for a place in the form of creative images, reputation and associations with innovation. While literature has long identified architectural uniqueness as a potential driver of brand competitiveness, we argue diffusion of that image is equally important. This work draws upon economic concepts from other cultural industries (such as film, music and art) to develop a framework for understanding how cultural brands are built: How reproducible images of singular architecture accumulate in the media to strengthen a brand. We then test an art brand’s impact on visitors. This work aims to offer evidence that the Guggenheim Museum Bilbao brand generates tourism to the city of Bilbao. By understanding how iconic cultural infrastructures create symbolic capital, policy makers may better tailor similar culture-led branding strategies to other places.

The seasonal stability tests of Canova and Hansen (1995) (CH) provide a method complementary to that of Hylleberg et al. (1990) for testing for seasonal unit roots. But the distributions of the CH tests are unknown for small samples. We present a method for numerically computing critical values and P-values for the CH tests for any sample size and any seasonal periodicity. In fact, this method is applicable to the types of seasonality which are commonly in use, but also to any other. (C) 2013 Elsevier B.V. All rights reserved.

Purpose – Online advertising such as Google AdWords gives small and medium-sized enterprises access to new markets at reduced costs. The purpose of this paper is to analyse the visibility and performance of a website and to test the effectiveness of online marketing using the data provided by Google Analytics. Design/methodology/approach – The authors use a class of econometric time series models with unobservable components, Structural Time Series Models (STSM). The authors allow for time-varying trends to take into account the non-stationary behaviour displayed by time series. The authors illustrate the model using daily data from a local tourist website. Three specific questions are addressed: do paid keywords campaigns increase the volume and quality of search traffic? Do paid keywords affect the volume and quality of the unpaid traffic? How do paid and unpaid keywords perform? Findings – The results for the case study show that: first, online campaigns affect traffic volume positively but their effectiveness on traffic quality is uncertain; second, paid keywords do not affect the volume and quality of unpaid traffic; third, the increase in traffic volume is not always due to the paid keywords and the lowest quality visits come from paid traffic. Practical implications – This analysis may help webmasters to design successful online advertising strategies. Originality/value – This study contributes to the development of user-friendly methodologies to monitor website performance. The analysis shows that STSM is a suitable methodology to test the effectiveness of online campaigns and to assess the changes over time in the performance of a website.

This paper studies whether non-separabilities between consumption and leisure may help to explain the observed persistence in GNP growth. We consider an extended version of Lucas's (1988) human capital investment model that includes labour adjustment costs and compare its performance under different utility specifications with different degrees of complementarity and substitutability between consumption and leisure. We find that when consumption and leisure are complements the model succeeds in matching not only the autocorrelation of output growth but also the important trend-reverting component found in US data. These results hold even if low adjustment costs of labour are considered. Hence, we conclude that an arguably simple margin not considered conventionally can provide useful insights into observed business cycle patterns.

This paper studies the evolution of the external demand for spanish tourist services. The analysis is carried out within the framework of Structural Time Series Models that are formulated in terms of unobserved components stochastically specified. The first aim of this work is to analyse tourism trends. A measure of the underlying rate of growth of the international demand is derived in order to evaluate whether the sector is in a period of expansion or recession. The empirical results show that the worst period of the crisis suffered at the end of the eighties by the tourist industry is over now and the future prospects are quite optimistic in the short run. The second aim of this paper is to perform an econometric analysis of the tourism demand in Spain in order to understand the major facts which influence visitation levels. The estimated structural model includes as explanatory variables an income index, two price indexes (one with respect to client countries and another with respect to competitor countries), a stochastic trend, representing the changes in tourist tastes, and a stochastic seasonal component. The forecasting performance of the estimated structural model compares well with the forecasting performance of two alternative dynamic models, the transfer function and error correction models.

This paper studies the evolution of the international tourism demand for Spain in order to forecast its trends. The analysis is carried out within the framework of structural time series models that are formulated in terms of unobserved components stochastically specified. A measure of the underlying rate of growth of the international demand is derived in order to evaluate whether the sector is In a period of expansion or recession. The empirical results show that the worst period of the crisis suffered at the end of the 80s by the industry is over now and the future prospects are optimistic in the short run. Copyright (C) 1996 Elsevier Science Ltd

In this paper the external demand for Spanish tourist services is analysed within the framework of Structural Time Series Models. The estimated structural model includes as explanatory variables an income index, two price indexes (one with respect to client countries and another with respect to competitor countries), a stochastic trend, representing the changes in tourist tastes, and a stochastic seasonal component. The results show that both price indexes are the more relevant of the variables that determine tourist demand and that the contribution of the trend component has been decisive in the rapid rates of growth of the tourist sector during recent years. The forecasting performance of the estimated structural model compares well with the forecasting performance of two alternative dynamic models, the transfer function and error correction models.