Why common factors in international bond returns are not so common

This paper analyzes the common factor structure of US, German, and Japanese Government bond returns. Unlike previous studies, we formally take into account the presence of country-specific factors when estimating common factors. We show that the classical approach of running a principal component analysis on a multi-country dataset of bond returns captures both local and common influences and therefore tends to pick too many factors. We conclude that US bond returns share only one common factor with German and Japanese bond returns. This single common factor is associated most notably with changes in the level of domestic term structures. We show that accounting for country-specific factors improves the performance of domestic and international hedging strategies.

Estimating cointegrated panels with common factors and the forward rate unbiasedness hypothesis

This article proposes a bias-adjusted estimator for use in cointegrated panel regressions when the errors are cross-sectionally correlated through an unknown common factor structure. The asymptotic distribution of the new estimator is derived and is examined in small samples using Monte Carlo simulations. For the estimation of the number of factors, several information-based criteria are considered. The simulation results suggest that the new estimator performs well in comparison to existing ones. In our empirical application, we provide new evidence suggesting that the forward rate unbiasedness hypothesis cannot be rejected. © The Author 2007.

Common factors in Latin America's business cycles

This paper constructs new business cycle indices for Argentina, Brazil, Chile, and Mexico based on common dynamic factors extracted from a comprehensive set of sectoral output, external trade, fiscal and financial variables. The analysis spans the 135 years since the insertion of these economies into the global economy in the 1870s. The constructed indices are used to derive a business cyc1e chronology for these countries and characterize a set of new stylized facts. In particular, we show that ali four countries have historically displayed a striking combination of high business cyc1e volatility and persistence relative to advanced country benchmarks. Volatility changed considerably over time, however, being very high during early formative decades through the Great Depression, and again during the 1970s and ear1y 1980s, before declining sharply in three of the four countries. We also identify a sizeable common factor across the four economies which variance decompositions ascribe mostly to foreign interest rates and shocks to commodity terms of trade.

Common factors method to predict the carcass composition tissue in kid goats

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Towards a taxonomy of common factors in psychotherapy-results of an expert survey

BACKGROUND: How change comes about is hotly debated in psychotherapy research. One camp considers 'non-specific' or 'common factors', shared by different therapy approaches, as essential, whereas researchers of the other camp consider specific techniques as the essential ingredients of change. This controversy, however, suffers from unclear terminology and logical inconsistencies. The Taxonomy Project therefore aims at contributing to the definition and conceptualization of common factors of psychotherapy by analyzing their differential associations to standard techniques. METHODS: A review identified 22 common factors discussed in psychotherapy research literature. We conducted a survey, in which 68 psychotherapy experts assessed how common factors are implemented by specific techniques. Using hierarchical linear models, we predicted each common factor by techniques and by experts' age, gender and allegiance to a therapy orientation. RESULTS: Common factors differed largely in their relevance for technique implementation. Patient engagement, Affective experiencing and Therapeutic alliance were judged most relevant. Common factors also differed with respect to how well they could be explained by the set of techniques. We present detailed profiles of all common factors by the (positively or negatively) associated techniques. There were indications of a biased taxonomy not covering the embodiment of psychotherapy (expressed by body-centred techniques such as progressive muscle relaxation, biofeedback training and hypnosis). Likewise, common factors did not adequately represent effective psychodynamic and systemic techniques. CONCLUSION: This taxonomic endeavour is a step towards a clarification of important core constructs of psychotherapy. KEY PRACTITIONER MESSAGE: This article relates standard techniques of psychotherapy (well known to practising therapists) to the change factors/change mechanisms discussed in psychotherapy theory. It gives a short review of the current debate on the mechanisms by which psychotherapy works. We provide detailed profiles of change mechanisms and how they may be generated by practice techniques.

Blooms of the cyanobacterium Lyngbya majuscula in coastal Queensland, Australia: disparate sites, common factors

During the last decade there has been a significant rise in observations of blooms of the toxic cyanobacterium, Lyngbya majuscula along the east coast of Queensland, Australia. Whether the increase in cyanobacterial abundance is a biological indicator of widespread water quality degradation or also a function of other environmental change is unknown. A bioassay approach was used to assesses the potential for runoff from various land uses to stimulate productivity of L. majuscula. In Moreton Bay, L. majuscula productivity was significantly (p < 0.05) stimulated by soil extracts, which were high in phosphorus, iron and organic carbon. Productivity of L. majuscula from the Great Barrier Reef was also significantly (p < 0.05) elevated by iron and phosphorus rich extracts, in this case seabird guano adjacent to the bloom site. Hence, it is possible that other L. majuscula blooms are a result of similar stimulating factors (iron, phosphorus and organic carbon), delivered through different mechanisms. (c) 2004 Elsevier Ltd. All rights reserved.

Factors contributing to nurse job satisfaction in the acute hospital setting : a review of recent literature

Aim  To explore and discuss from recent literature the common factors contributing to nurse job satisfaction in the acute hospital setting. Background  Nursing dissatisfaction is linked to high rates of nurses leaving the profession, poor morale, poor patient outcomes and increased financial expenditure. Understanding factors that contribute to job satisfaction could increase nurse retention. Evaluation  A literature search from January 2004 to March 2009 was conducted using the keywords nursing, (dis)satisfaction, job (dis)satisfaction to identify factors contributing to satisfaction for nurses working in acute hospital settings. Key issues  This review identified 44 factors in three clusters (intra-, inter- and extra-personal). Job satisfaction for nurses in acute hospitals can be influenced by a combination of any or all of these factors. Important factors included coping strategies, autonomy, co-worker interaction, direct patient care, organizational policies, resource adequacy and educational opportunities. Conclusions  Research suggests that job satisfaction is a complex and multifactorial phenomenon. Collaboration between individual nurses, their managers and others is crucial to increase nursing satisfaction with their job. Implications for nursing management  Recognition and regular reviewing by nurse managers of factors that contribute to job satisfaction for nurses working in acute care areas is pivotal to the retention of valued staff.

Rebuilding housing after a disaster : factors for failure

Unlike most normal construction projects, post-disaster housing projects are diverse in nature, have unique socio-cultural and economical requirements, and are extremely dynamic and thus necessitate a meaningful and dynamic response. Post-disaster reconstruction practices that lack a strategy compatible with the severity of disaster, community culture, socio-economic requirements, environmental condition, government legislations, and technical and technological situations, often fail to operate and respond effectively to the needs of the wider affected population. Factors that frequently pose real threats to the eventual success of reconstruction projects are rarely given appropriate consideration when designing such projects. Research into past reconstruction practices has shown that ignoring these factors altogether or failing to give them meaningful consideration can affect housing reconstruction projects. In other words, they either miss their targets altogether or undergo serious modifications after their occupancy, subsequently resulting in an overall loss of project resources. This article touches upon the common factors that negatively impact the outcome of such projects.

Testing for a Unit Root in Panels with Dynamic Factors

This paper studies testing for a unit root for large n and T panels in which the cross-sectional units are correlated. To model this cross-sectional correlation, we assume that the data is generated by an unknown number of unobservable common factors. We propose unit root tests in this environment and derive their (Gaussian) asymptotic distribution under the null hypothesis of a unit root and local alternatives. We show that these tests have significant asymptotic power when the model has no incidental trends. However, when there are incidental trends in the model and it is necessary to remove heterogeneous deterministic components, we show that these tests have no power against the same local alternatives. Through Monte Carlo simulations, we provide evidence on the finite sample properties of these new tests.

On the determination of the number of factors using information criteria with data-driven penalty

As is well known, when using an information criterion to select the number of common factors in factor models the appropriate penalty is generally indetermine in the sense that it can be scaled by an arbitrary constant, c say, without affecting consistency. In an influential paper, Hallin and Liška (J Am Stat Assoc102:603–617, 2007) proposes a data-driven procedure for selecting the appropriate value of c. However, by removing one source of indeterminacy, the new procedure simultaneously creates several new ones, which make for rather complicated implementation, a problem that has been largely overlooked in the literature. By providing an extensive analysis using both simulated and real data, the current paper fills this gap.

Estimation of factor-augmented panel regressions with weakly influential factors

The use of factor-augmented panel regressions has become very popular in recent years. Existing methods for such regressions require that the common factors are strong, such that their cumulative loadings rise proportionally to the number of cross-sectional units, which of course need not be the case in practice. Motivated by this, the current paper offers an indepth analysis of the effect of non-strong factors on two of the most popular estimators for factor-augmented regressions, namely, principal components (PC) and common correlated effects (CCE).