No abstract

This survey provides an overview with a broad coverage of the literature on methods for temporal disaggregation and benchmarking. Dozens of methods, procedures and algorithms have been proposed in the statistical and economic literature to solve the problem of transforming a low-frequency series into a high-frequency one. This paper classifies and reviews the procedures, provides interesting discussion on the history of the methodological development in this literature and permits to identify the assets and drawbacks of each method, to comprehend the current state of art on the subject and to identify the topics in need of further development. It would be useful for readers who are interested in the techniques but are not yet familiar with the literature and also for researchers who would like to keep up with the recent developments in this area. After reading the article the reader should have a good understanding of the most important approaches, their shortcomings and advantages, and be able to make an informed judgment on which methods are most suitable for his or her purpose. Interested readers, however, will not find much detail of the methods reviewed. Due to the broadness of the subjects and the large number of studies being referenced, it is provided some general assessments on the methods revised without great detailed analysis. This review article could serve as a brief introduction to the literature on temporal disaggregation

Subnational regional jurisdictions rarely have at their disposal a reasonable array of timely statistics to monitor their economic condition. In light of this, we develop a procedure that simultaneously estimates a quarterly time series for all regions of a country based upon quarterly national and annual regional data. While other such techniques exist, we suggest a temporal error structure that eliminates possible spurious jumps. Using our approach, regional analysts should now be able to distribute national growth among regions as soon as quarterly national figures are released. In a Spanish application, we detail some practicalities of the process and show that our proposal produces better estimates than the uniregional methods often used.  Copyright © 2007 John Wiley & Sons. Ltd.

Countless examples of misleading forecasts on behalf of both campaign and exit polls affecting, among others, British, French, and Spanish elections could be found. This has seriously damaged their image. Therefore, procedures should be used that minimize errors, especially on election night when errors are more noticeable, in order to maintain people's trust in surveys. This paper proposes a method to obtain quick and early outcome forecasts on the election night. The idea is to partly sample some (whatever) polling stations and use the consistency that polling stations show between elections to predict the final results. Model accuracy is analysed through simulation using seven different types of samples in four elections. The efficacy of the technique is also tested predicting the 2005 Eusko Legebiltzarra elections from real data. Results confirm that the procedure generates highly reliable and accurate forecasts. Furthermore, compared with the classical quick count strategy, the method is revealed as much more robust and precise.election forecasts, error observation, generalized linear regression, pseudodata augmentation, Spanish elections,

The most important asset of any organisation or country is its reserve of human resource. The capacity of society to understand and successfully adapt to new situations is highly related to the levels of training and education of its individual members. The education system, therefore, is one of the main foundations of any society's future. Over the last decade, Spain and the Valencia region, in particular, have experienced precipitous drops in fertility that will inevitably reduce school enrolments. In this paper we quantify the magnitude of the reductions and, after some analysis, lay out the potential consequences for the regional education system, and for society as a whole.