The Global Side of the Investment-Savings Puzzle

In this paper we re-examine the long standing and puzzling correlation between national savings and investment in industrial countries. We apply an econometric methodology that allows us to separate idiosyncratic correlation at the country level from correlation at the global level. In a major break with the existing literature, we find no evidence of a long run relationship in the idiosyncratic components of savings and investment. We also find that the global components in savings and investments comove, indicating that they react to shocks of a global nature.

Common and Idiosyncratic Factors of the Exchange Risk Premium in Emerging European Markets

Existing empirical evidence suggests that the Uncovered Interest Rate Parity (UIRP) condition may not hold due to an exchange risk premium. For a panel data set of eleven emerging European economies we decompose this exchange risk premium into an idiosyncratic (country-specific) elements and a common factor using a principal components approach. We present evidence of a stationary idiosyncratic component and nonstationary common factor. This result leads to the conclusion of a nonstationary risk premium for these countries and a violation of the UIRP in the long-run, which is in contrast to previous studies often documenting a stationary premium in developed countries. Furthermore, we report that the variation in the premium is largely attributable to a common factor influenced by economic developments in the United States.

The Change Questionnaire predicts change in hazardous tobacco and alcohol use.

INTRODUCTION: Assessing motivation for change is deemed an important step in the treatment process that allows further refinement of the intervention in motivational interviewing (MI) and brief MI (BMI) adaptations. During MI (and BMI) sessions, motivation for change is expressed by the client as "change talk", i.e. all statements inclined toward or away from change. We tested the predictive validity of the Change Questionnaire, a 12-item instrument assessing motivation to change, on hazardous tobacco and alcohol use. METHODS: As part of the baseline measurements for a randomized controlled trial on multi-substance BMI at the Lausanne recruitment center (army conscription is mandatory in Switzerland for males at age 20, and thus provides a unique opportunity to address a non-clinical and largely representative sample of young men), 213 participants completed the questionnaire on tobacco and 95 on alcohol and were followed-up six months later. The overall Change Questionnaire score and its six subscales (Desire, Ability, Reasons, Need, Commitment, and Taking steps) were used as predictors of hazardous tobacco use (defined as daily smoking) and hazardous alcohol use (defined as more than one occasion with six standard drinks or more per month, and/or more than 21 standard drinks per week) in bivariate logistic regression models at follow-up. RESULTS: Higher overall Change scores were significant predictors of decreased risk for hazardous tobacco (odds ratio [OR] = 0.83, p = 0.046) and alcohol (OR = 0.76, p = 0.03) use. Several sub-dimensions were associated with the outcomes in bivariate analyses. Using a principal components analysis to reduce the number of predictors for multivariate models, we obtained two components. 'Ability to change' was strongly related to change in hazardous tobacco use (OR = 0.54, p < 0.001), the second we interpreted as 'Other change language dimensions' and which was significantly related to change in hazardous alcohol use (OR = 0.81, p = 0.05). CONCLUSIONS: The present findings lend initial support for the predictive validity of the Change Questionnaire on hazardous tobacco and alcohol use, making it an interesting and potentially useful tool for assessing motivation to change among young males.

Leading indicator properties of US high-yield credit spreads

In this paper we examine the out-of-sample forecast performance of high-yield credit spreads regarding real-time and revised data on employment and industrial production in the US. We evaluate models using both a point forecast and a probability forecast exercise. Our main findings suggest the use of few factors obtained by pooling information from a number of sector-specific high-yield credit spreads. This can be justified by observing that, especially for employment, there is a gain from using a principal components model fitted to high-yield credit spreads compared to the prediction produced by benchmarks, such as an AR, and ARDL models that use either the term spread or the aggregate high-yield spread as exogenous regressor. Moreover, forecasts based on real-time data are generally comparable to forecasts based on revised data. JEL Classification: C22; C53; E32 Keywords: Credit spreads; Principal components; Forecasting; Real-time data.

Is there a common factor for vision?

In cognition, common factors play a crucial role. For example, different types of intelligence are highly correlated, pointing to a common factor, which is often called g. One might expect that a similar common factor would also exist for vision. Surprisingly, no one in the field has addressed this issue. Here, we provide the first evidence that there is no common factor for vision. We tested 40 healthy students' performance in six basic visual paradigms: visual acuity, vernier discrimination, two visual backward masking paradigms, Gabor detection, and bisection discrimination. One might expect that performance levels on these tasks would be highly correlated because some individuals generally have better vision than others due to superior optics, better retinal or cortical processing, or enriched visual experience. However, only four out of 15 correlations were significant, two of which were nontrivial. These results cannot be explained by high intraobserver variability or ceiling effects because test-retest reliability was high and the variance in our student population is commensurate with that from other studies with well-sighted populations. Using a variety of tests (e.g., principal components analysis, Bayes theorem, test-retest reliability), we show the robustness of our null results. We suggest that neuroplasticity operates during everyday experience to generate marked individual differences. Our results apply only to the normally sighted population (i.e., restricted range sampling). For the entire population, including those with degenerate vision, we expect different results.

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

Detecció i localització d'impactes a estructures aeroespacials

Projecte de recerca elaborat a partir d’una estada a la Universidad Politécnica de Madrid, Espanya, entre setembre i o desembre del 2007. Actualment la indústria aeroespacial i aeronàutica té com prioritat millorar la fiabilitat de las seves estructures a través del desenvolupament de nous sistemes per a la monitorització i detecció d’impactes. Hi ha diverses tècniques potencialment útils, i la seva aplicabilitat en una situació particular depèn críticament de la mida del defecte que permet l’estructura. Qualsevol defecte canviarà la resposta vibratòria de l’element estructural, així com el transitori de l’ona que es propaga per l’estructura elàstica. Correlacionar aquests canvis, que poden ser detectats experimentalment amb l’ocurrència del defecte, la seva localització i quantificació, és un problema molt complex. Aquest treball explora l’ús de l'Anàlisis de Components Principals (Principal Component Analysis - PCA-) basat en la formulació dels estadístics T2 i Q per tal de detectar i distingir els defectes a l'estructura, tot correlacionant els seus canvis a la resposta vibratòria. L’estructura utilitzada per l’estudi és l’ala d’una turbina d’un avió comercial. Aquesta ala s’excita en un extrem utilitzant un vibrador, i a la qual s'han adherit set sensors PZT a la superfície. S'aplica un senyal conegut i s'analitzen les respostes. Es construeix un model PCA utilitzant dades de l’estructura sense defecte. Per tal de provar el model, s'adhereix un tros d’alumini en quatre posicions diferents. Les dades dels assajos de l'estructura amb defecte es projecten sobre el model. Les components principals i les distàncies de Q-residual i T2-Hotelling s'utilitzaran per a l'anàlisi de les incidències. Q-residual indica com de bé s'adiu cadascuna de les mostres al model PCA, ja que és una mesura de la diferència, o residu, entre la mostra i la seva projecció sobre les components principals retingudes en el model. La distància T2-Hotelling és una mesura de la variació de cada mostra dins del model PCA, o el que vindria a ser el mateix, la distància al centre del model PCA.

Morphometric discrimination of females of five species of Anopheles of the subgenus Nyssorhynchus from Southern and Northwest Colombia

The most important vectors of human Plasmodium in the neotropics belong to the subgenus Nyssorhynchus. These species are generally sympatric in terms of their geographical distributions. Some are difficult to identify based solely on examination of adult females using the available morphological keys, in these cases examination of immature stages and male genitalia is required to make correct determinations. However, in epidemiological studies it is necessary to identify the species of adult females which are found near humans, i.e. in studies of malaria transmission or evaluation of control measures. The purpose of the present study was to evaluate the discrimination of adult females of different species of Nyssorhynchus isolated mainly from Southern Colombia (department of Putumayo), using morphometric analysis. Adult females were obtained after rearing larvae collected in natural breeding places and from the progeny of females collected on humans. The morphological characteristics of the immature stages allowed the identification of four species of the subgroup Oswaldoi from Southern Colombia: Anopheles rangeli Gabaldon, Cova Garcia & Lopez, An. oswaldoi (Peryassu), An. benarrochi Gabaldon, Cova Garcia & Lopez and An. triannulatus (Neiva & Pinto). The species An. nuneztovari (Gabaldon) from the Northwest of Colombia was included for comparison. Morphometric analysis allowed differentiation of the females of all species to a confidence level approaching 90% using principal components analysis of 10 wing and leg variables, followed by canonical variate analysis of the first four principal components. We conclude that morphometrics may represent a useful taxonomic tool for this group and that its use should be further studied.

Attachment and temperament in early childhood; implications for later behavior problems.

In the context of a French validation study, the Child Behavior Checklist (CBCL) was administered to more than 3000 French speaking mothers of 5-year-old children. Scores were factor-analyzed. Principal components analysis revealed four dimensions: externalizing and internalizing behavior problems, immaturity and somatoform disorders. Another sample of 40 mothers participated in a longitudinal study, filling in the CBCL when their children were 5 years old. These children had been observed previously in the Strange Situation (SSP) at 21 months. Several dichotomous variables derived from the SSP (e.g. secure versus insecure, proximal versus distal interaction with the mother, avoidant behavior) have been used as predictors of the four dimensions extracted from the CBCL. Hierarchical regressions showed that proximal behaviors with the mother, which reflect temperamental characteristics independently of the quality of attachment, predicted internalizing problems, whereas avoidance of the mother, or insecure-avoidant attachment, predicted internalizing as well as externalizing problems at 5 years of age. These results show that attachment and temperament, as assessed by the SSP, may each have specific implications for later behavior problems.

Indoor air microbiological evaluation of offices, hospitals, industries, and shopping centers

In this study it was compared the MAS-100 and the Andersen air samplers' performances and a similar trend in both instruments was observed. It was also evaluated the microbial contamination levels in 3060 samples of offices, hospitals, industries, and shopping centers, in the period of 1998 to 2002, in Rio de Janeiro city. Considering each environment, 94.3 to 99.4% of the samples were the allowed limit in Brazil (750 CFU/m³). The industries' results showed more important similarity among fungi and total heterotrophs distributions, with the majority of the results between zero and 100 CFU/m³. The offices' results showed dispersion around 300 CFU/m³. The hospitals' results presented the same trend, with an average of 200 CFU/m³. Shopping centers' environments showed an average of 300 CFU/m³ for fungi, but presented a larger dispersion pattern for the total heterotrophs, with the highest average (1000 CFU/m³). It was also investigated the correlation of the sampling period with the number of airborne microorganisms and with the environmental parameters (temperature and air humidity) through the principal components analysis. All indoor air samples distributions were very similar. The temperature and air humidity had no significant influence on the samples dispersion patterns.

An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology

Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.

Area deprivation and mortality in the provincial capital cities of Andalusia and Catalonia (Spain)

Objective: To study the linkage between material deprivation and mortality from all causes, for men and women separately, in the capital cities of the provinces in Andalusia and Catalonia (Spain). Methods: A small-area ecological study was devised using the census section as the unit for analysis. 188 983 Deaths occurring in the capital cities of the Andalusian provinces and 109 478 deaths recorded in the Catalan capital cities were examined. Principal components factorial analysis was used to devise a material deprivation index comprising the percentage of manual labourers, unemployment and illiteracy. A hierarchical Bayesian model was used to study the relationship between mortality and area deprivation. Main results: In most cities, results show an increased male mortality risk in the most deprived areas in relation to the least depressed. In Andalusia, the relative risks between the highest and lowest deprivation decile ranged from 1.24 (Malaga) to 1.40 (Granada), with 95% credibility intervals showing a significant excess risk. In Catalonia, relative risks ranged between 1.08 (Girona) and 1.50 (Tarragona). No evidence was found for an excess of female mortality in most deprived areas in either of the autonomous communities. Conclusions: Within cities, gender-related differences were revealed when deprivation was correlated geographically with mortality rates. These differences were found from an ecological perspective. Further research is needed in order to validate these results from an individual approach. The idea to be analysed is to identify those factors that explain these differences at an individual level.

Identifying pesticide use patterns among flower growers to assess occupational exposure to mixtures

Objectives Exposure assessment to a single pesticide does not capture the complexity of the occupational exposure. Recently, pesticide use patterns analysis has emerged as an alternative to study these exposures. The aim of this study is to identify the pesticide use pattern among flower growers in Mexico participating in the study on the endocrine and reproductive effects associated with pesticide exposure. Methods A cross-sectional study was carried out to gather retrospective information on pesticide use applying a questionnaire to the person in charge of the participating flower growing farms. Information about seasonal frequency of pesticide use (rainy and dry) for the years 2004 and 2005 was obtained. Principal components analysis was performed. Results Complete information was obtained for 88 farms and 23 pesticides were included in the analysis. Six principal components were selected, which explained more than 70% of the data variability. The identified pesticide use patterns during both years were: 1. fungicides benomyl, carbendazim, thiophanate and metalaxyl (both seasons), including triadimephon during the rainy season, chlorotalonyl and insecticide permethrin during the dry season; 2. insecticides oxamyl, biphenthrin and fungicide iprodione (both seasons), including insecticide methomyl during the dry season; 3. fungicide mancozeb and herbicide glyphosate (only during the rainy season); 4. insecticides metamidophos and parathion (both seasons); 5. insecticides omethoate and methomyl (only rainy season); and 6. insecticides abamectin and carbofuran (only dry season). Some pesticides do not show a clear pattern of seasonal use during the studied years. Conclusions The principal component analysis is useful to summarise a large set of exposure variables into smaller groups of exposure patterns, identifying the mixtures of pesticides in the occupational environment that may have an interactive effect on a particular health effect.

Comparing methods for dimensionality reduction when data are density functions

Functional Data Analysis (FDA) deals with samples where a whole function is observedfor each individual. A particular case of FDA is when the observed functions are densityfunctions, that are also an example of infinite dimensional compositional data. In thiswork we compare several methods for dimensionality reduction for this particular typeof data: functional principal components analysis (PCA) with or without a previousdata transformation and multidimensional scaling (MDS) for diferent inter-densitiesdistances, one of them taking into account the compositional nature of density functions. The difeerent methods are applied to both artificial and real data (householdsincome distributions)

An R Library for Compositional Data Analysis in Archaeometry

Compositional data naturally arises from the scientific analysis of the chemicalcomposition of archaeological material such as ceramic and glass artefacts. Data of thistype can be explored using a variety of techniques, from standard multivariate methodssuch as principal components analysis and cluster analysis, to methods based upon theuse of log-ratios. The general aim is to identify groups of chemically similar artefactsthat could potentially be used to answer questions of provenance.This paper will demonstrate work in progress on the development of a documentedlibrary of methods, implemented using the statistical package R, for the analysis ofcompositional data. R is an open source package that makes available very powerfulstatistical facilities at no cost. We aim to show how, with the aid of statistical softwaresuch as R, traditional exploratory multivariate analysis can easily be used alongside, orin combination with, specialist techniques of compositional data analysis.The library has been developed from a core of basic R functionality, together withpurpose-written routines arising from our own research (for example that reported atCoDaWork'03). In addition, we have included other appropriate publicly availabletechniques and libraries that have been implemented in R by other authors. Availablefunctions range from standard multivariate techniques through to various approaches tolog-ratio analysis and zero replacement. We also discuss and demonstrate a smallselection of relatively new techniques that have hitherto been little-used inarchaeometric applications involving compositional data. The application of the libraryto the analysis of data arising in archaeometry will be demonstrated; results fromdifferent analyses will be compared; and the utility of the various methods discussed