Suchergebnisse

The objective of this paper is to compare different forecasting methods for the short run forecasting of Business Survey Indicators. We compare the forecasting accuracy of Artificial Neural Networks -ANN- vs. three different time series models: autoregressions -AR-, autoregressive integrated moving average -ARIMA- and self-exciting threshold autoregressions -SETAR-. We consider all the indicators of the question related to a country's general situation regarding overall economy, capital expenditures and private consumption -present judgement, compared to same time last year, expected situation by the end of the next six months- of the World Economic Survey -WES- carried out by the Ifo Institute for Economic Research in co-operation with the International Chamber of Commerce. The forecast competition is undertaken for fourteen countries of the European Union. The main results of the forecast competition are offered for raw data for the period ranging from 1989 to 2008, using the last eight quarters for comparing the forecasting accuracy of the different techniques. ANN and ARIMA models outperform SETAR and AR models. Enlarging the observed time series of Business Survey Indicators is of upmost importance in order of assessing the implications of the current situation and its use as input in quantitative forecast models.

Pacto y consenso en la cultura política medieval / José Manuel Nieto Soria -- Entre hermanos, la difícil situación de Fernando I / Emmanuelle Klinka -- Consenso, pacto, amistad y seguridad. Escrituras y tácticas nobiliarias en la Castilla del siglo xv / M. Concepción Quintanilla Raso -- Expresiones de consentimiento e ideología feudal en las tomas de posesión señoriales de la Castilla Trastámara / Pablo Martín Prieto -- Monarquía, nobleza y pacto fiscal: lógicas contractuales y estrategias de consenso en torno al sistema hacendístico castellano (1429-1480) / Pablo Ortego Rico -- Ritualidad y cultura del consenso en la iglesia castellana bajomedieval / Jorge Díaz Ibáñez -- Las labores diplomáticas de los confesores de los reyes de Castilla al servicio de la monarquía: siglos xiv-xv / Guillermo F. Arquero Caballero -- Negociación y representación del consenso: los conflictos de época de Juan II de Castilla / Óscar Villarroel González -- "Et ut pacem concordiam inter eos reformaremus": pacto y consenso en el entorno del monasterio de Sahagún (siglos xi-xiii) / Charles Garcia -- Los escenarios del pacto y de la negociación política: la corona de Castilla (1369-1506) / David Nogales Rincón -- Pacto y cultura de consenso en la Castilla de Juan II: la familia Romero, una dinastía de oficiales al servicio de la corona (ca. 1407-ca. 1465) / Francisco de Paula Cañas Gálvez -- Los pactos de acceso a la ciudadanía. Una aproximación a su estudio a partir del caso tarraconense / Eduard Juncosa Bonet -- 1465: "para que sean e estén para la corona real". Pacto político, realengo concejil y guerra civil en Castilla / José Antonio Jara Fuente -- La concordia y el bien común en los pactos y acuerdos de la vida política de las ciudades castellanas de la Baja Edad Media / María Araujo González -- "Cuando se començaron a fablar entendiéronse e acompañáronse [...] e estonces acordaron amos en uno que escribiesen esta estoria". La práctica del consenso como operación historiográfica / Frédéric Alchalabi -- Consenso y disenso en la crónica de Enrique IV de Diego Enriquez del Castillo / María del Pilar Rábade Obradó -- Imágenes del turco en la Castilla del siglo xv / Luis Fernández Gallardo -- Visiones artísticas y consenso político en la Corona de Castilla. Lo funerario en la Baja Edad Media / Olga Pérez Monzón

La dependencia y el trabajo de cuidados se están situando en un plano central dentro del debate académico. Antaño, diversos factores incidieron en un mayor recurso a la cobertura mercantil de las necesidades crecientes en este campo, pero con la crisis económica iniciada a finales del 2007 se ha transitado hacia la desmercantilización/refamiliarización de los cuidados, consecuencia del elevado desempleo y de las políticas austericidas. La investigación descansa en la realización de once entrevistas en profundidad semiestructuradas a actores clave de la dependencia del Àrea Metropolitana de València, previo análisis exploratorio. Se llega a la conclusión que (a) el cuidado en el ámbito doméstico es el más frecuente; (b) los principales factores para la elección de la modalidad de los cuidados son los de tipo económico y los culturales; (c) la mujer continúa como protagonista principal del cuidado en el ámbito reproductivo y es la que debe enfrentarse a la compatibilización de la vida laboral y familiar, con repercusiones negativas graves en su presencia y resultados alcanzados en el mercado de trabajo; además, (d) la falta de capacidad económica de un buen número de familias les ha conducido a abandonar el recurso al mercado, lo que, unido a la carencia de una oferta de servicios públicos suficiente, ha facilitado un desplazamiento hacia la refamiliarización del cuidado. En otros casos se han buscado alternativas mercantiles más económicas, con bastante frecuencia en contextos de informalidad, entre las que destaca la (re)nativización o retorno de la mujer autóctona a estas actividades de forma remunerada.

In this study, we introduce a sentiment construction method based on the evolution of survey-based indicators. We make use of genetic algorithms to evolve qualitative expectations in order to generate country-specific empirical economic sentiment indicators in the three Baltic republics and the European Union. First, for each country we search for the non-linear combination of firms' and households' expectations that minimises a fitness function. Second, we compute the frequency with which each survey expectation appears in the evolved indicators and examine the lag structure per variable selected by the algorithm. The industry survey indicator with the highest predictive performance are production expectations, while in the case of the consumer survey the distribution between variables is multi-modal. Third, we evaluate the out-of-sample predictive performance of the generated indicators, obtaining more accurate estimates of year-on-year GDP growth rates than with the scaled industrial and consumer confidence indicators. Finally, we use non-linear constrained optimisation to combine the evolved expectations of firms and consumers and generate aggregate expectations of of year-on-year GDP growth. We find that, in most cases, aggregate expectations outperform recursive autoregressive predictions of economic growth.

In this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon.

This study aims to analyze the effects of data pre-processing on the performance of forecasting based on neural network models. We use three different Artificial Neural Networks techniques to forecast tourist demand: a multi-layer perceptron, a radial basis function and an Elman neural network. The structure of the networks is based on a multiple-input multiple-output setting (i.e. all countries are forecasted simultaneously). We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.

This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other Artificial Intelligence techniques based on statistical learning. We use two different neural networks and three SVR models that differ by the type of kernel used. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian kernel shows the best forecasting performance. The best predictions are obtained for longer forecast horizons, which suggest the suitability of machine learning techniques for medium and long term forecasting.

In this study we construct quarterly consumer confidence indicators of unemployment for the euro area using as input the consumer expectations for sixteen socio-demographic groups elicited from the Joint Harmonised EU Consumer Survey. First, we use symbolic regressions to link unemployment rates to qualitative expectations about a wide range of economic variables. By means of genetic programming we obtain the combination of expectations that best tracks the evolution of unemployment for each group of consumers. Second, we test the out-of-sample forecasting performance of the evolved expressions. Third, we use a state-space model with time varying parameters to identify the main macroeconomic drivers of unemployment confidence and to evaluate whether the strength of the interplay between variables varies across the economic cycle. We analyse the differences across groups, obtaining better forecasts for respondents comprised in the first quartile with regards to the income of the household and respondents with at least secondary education. We also find that the questions regarding expected major purchases over the next 12 months and savings at present are by far, the variables that most frequently appear in the evolved expressions, hinting at their predictive potential to track the evolution of unemployment. For the economically deprived consumers, the confidence indicator seems to evolve independently of the macroeconomy. This finding is rather consistent throughout the economic cycle, with the exception of stock market returns, which governed unemployment confidence in the pre-crisis period.

The so-called urbanism of risk created the conditions for the inadequate urban planning in Mexico City (CDMX), which affects the populations established there. This term is little known today and should not be confused with urban risk. Nowadays, the incorrect urban configuration creates awareness in the inhabitants about the risks present in their lives, according to the impact of natural phenomena (hazards). It is important to understand that the effects of disasters in urban areas are derived from growth in places with unsuitable characteristics, due to the irregularity of the terrain. Risk is the result between hazard and vulnerability, which becomes urban risk when it affects the established population. The contribution of this article shows that the beginning of the adequate urban configuration based on geoinformatics systems will prevent risk urbanism in areas where there is no city growth yet. As a result, it is shown the crossing of variables provided by governmental institutions and real events occurred, according to floods, landslides, earthquakes and collapse of mined areas, using geoinformatics systems to show the municipalities with greater affectation, according to the existing urban risk, which can be replicated and attended in other parts of the country and the world, based on the management between government, academics and population, with the objective of preventing the creation of risk urbanism.

The Working Group on Technology Integration for Fishery-Dependent Data (WGTIFD) met in Copenhagen, Denmark, 7-9 May 2019 for its first meeting in its three-year multi-annual cycle. WGTIFD has diverse membership including technology service providers, academic and governmental marine institutions, and non-profit environmental organizations, across a wide range of EU, US, and Canadian fisheries. The WGTIFD's primary objective is to examine the electronic tools and applications that are used to support fisheries-dependent data collection, both on shore and at sea, including electronic reporting, electronic monitoring, positional data systems, and observer data collection. The primary objectives of the first meeting were to inventory and review the various national fisheries dependent hardware and software applications and approaches (ToR A); define and agree on consistent vocabulary on electronic technologies (ToR B); and report on developments in machine learning and computer vision technologies and their applications in fisheries dependent data collection (ToR E). The working group was able to develop a common vocabulary of terms that can be used within the ICES community, and conducted a survey of WGTIFD participants on their experience in implementing technology for monitoring and reporting programs, and their views on strategies and incentives to engage stakeholders. This Year 1 report provides a fairly robust assessment on the available electronic technologies and how they're being used in fisheries around the world, the successes and challenges with implementing these tools, and some of the existing applications for using machine learning for processing data in fisheries. WGTIFD also started to examine the risks and benefits of different technologies (ToR C), but does not make a full assessment or recommendation at this time. The same can be said for how to integrate data from technologies (ToR D). These topics will be examined in Year 2 and will be fully reported at the end of the multi-annual cycle. Many technologies in fisheries are relatively new, compared with traditional data collection programs, and the working group itself is new, making it difficult to determine the reach and impact of the Year 1 report. However, technology-based programs appear to be developing and expanding rapidly, and interest in future work of the group is growing too, so it is expected that the findings will have greater impact over time. Additionally, the intial work was intentional for developing a baseline of tools and vocabulary, and it is expected that work in Year 2 on exploring trade-offs of technologies and how the data is used, will be of more interest and to a wider audience. WGTIFD will be meeting in Galway, Ireland May 11-15, 2020 to expand their work.