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.

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).

The psychological impact of a cancer diagnosis on families: the influence of family functioning and patients' illness characteristics on depression and anxiety

A diagnosis of cancer is a very stressful event for the patients and their families. Patients, partners and other family members can suffer from clinical levels of depression and severe levels of anxiety and stress reactions. The similarity in levels of distress between patients and partners and patients and offspring suggests that there are common factors that impact on families' distress levels. The current study examined levels of depression and anxiety in newly diagnosed adult patients (n = 48) and their adult relatives (n = 99). Family functioning and patients' illness characteristics were identified as factors that might impact on families' depression and anxiety. Results from multilevel models indicated that family functioning was important. Families that were able to act openly, express feelings directly, and solve problems effectively had lower levels of depression. Direct communication of information within the family was associated with lower levels of anxiety. Aside from differences anxiety due to cancer type, patients' illness characteristics appear to be risk factors in patients' but not relatives' depression and anxiety. The results from the current study suggest that researchers and clinicians need to be family-focused as cancer affects the whole family, not just the patient.

Sources of liquidity commonality, asymmetric information and ownership structure in emerging markets : evidence from the Chinese stock market

Xinwei Zheng examines if common factors of liquidity can be determined by ownership structure measured by asymmetric information in an emerging market that has adopted an order-driven trading system. Using China as a case for the study, I select a broad sample of stocks from two separate Chinese stock exchanges to measure and
analyse the relationship. My empirical evidence seems significant and pervasive. These findings about the Chinese stock market provide useful pointers for understanding commonality in emerging economies and shed critical light
on a new dimension of the working of emerging markets.

Australian teaching and learning centres through the eyes of their directors : characteristics, capacities and constraints

This paper is based on research to identify common factors that contribute to the effective strategic leadership of teaching and learning centres. The second of three phases of data collection involved a survey of Directors of Australian teaching and learning centres. The data collected were quantitatively analysed using a range of descriptive, parametric and non-parametric techniques. Based on a response rate of 81.6 percent, we present a contemporary, comprehensive and representative quantitative snapshot of Australian teaching and learning centres, as seen through the eyes of their Directors. The time since last restructure, incumbency of the current Director and total Directorship experience of the current Centre Director all have mean values of ‘sometime in the previous one to three years’. Most Centres would consider their work in the areas of ‘recognition and reward’ and ‘professional development of staff’ as high impact functions, and they would be pleased with their efforts in the former area, and wish to perform better on the latter. The principal constraint identified by Centres was ‘lack of staff time’, both in the Faculties and in the Centre, to engage in teaching and learning improvement activities. Overall, Centres feel well included in relevant university committees and other activities.

Role assessment of GIS analysis and its reliability while ranking urban sustainability using scenarios specific to regional climate, community and culture

Urban Sustainability expresses the level of conservation of a city while living a town or consuming its urban resources, but the measurement of urban sustainability depends on what are considered important indicators of conservation besides the permitted levels of consumption in accordance with adopted criteria. This criterion should have common factors that are shared for all the members tested or cities to be evaluated as in this particular case for Abu Dhabi, but also have specific factors that are related to the geographic place, community and culture, that is the measures of urban sustainability specific to a middle east climate, community and culture where GIS Vector and Raster analysis have a role or add a value in urban sustainability measurements or grading are considered herein. Scenarios were tested using various GIS data types to replicate urban history (ten years period), current status and expected future of Abu Dhabi City setting factors to climate, community needs and culture. The useful Vector or Raster GIS data sets that are related to every scenario where selected and analysed in the sense of how and how much it can benefit the urban sustainability ranking in quantity and quality tests, this besides assessing the suitable data nature, type and format, the important topology rules to be considered, the useful attributes to be added, the relationships which should be maintained between data types of a geo- database, and specify its usage in a specific scenario test, then setting weights to each and every data type representing some elements of a phenomenon related to urban suitability factor. The results of assessing the role of GIS analysis provided data collection specifications such as the measures of accuracy reliable to a certain type of GIS functional analysis used in an urban sustainability ranking scenario tests. This paper reflects the prior results of the research that is conducted to test the multidiscipline evaluation of urban sustainability using different indicator metrics, that implement vector GIS Analysis and Raster GIS analysis as basic tools to assist the evaluation and increase of its reliability besides assessing and decomposing it, after which a hypothetical implementation of the chosen evaluation model represented by various scenarios was implemented on the planned urban sustainability factors for a certain period of time to appraise the expected future grade of urban sustainability and come out with advises associated with scenarios for assuring gap filling and relative high urban future sustainability. The results this paper is reflecting are concentrating on the elements of vector and raster GIS analysis that assists the proper urban sustainability grading within the chosen model, the reliability of spatial data collected; analysis selected and resulted spatial information. Starting from selecting some important indicators to comprise the model which include regional culture, climate and community needs an example of what was used is Energy Demand & Consumption (Cooling systems). Thus, this factor is related to the climate and it‟s regional specific as the temperature varies around 30-45 degrees centigrade in city areas, GIS 3D Polygons of building data used to analyse the volume of buildings, attributes „building heights‟, estimate the number of floors from the equation, following energy demand was calculated and consumption for the unit volume, and compared it in scenario with possible sustainable energy supply or using different environmental friendly cooling systems this is followed by calculating the cooling system effects on an area unit selected to be 1 sq. km, combined with the level of greenery area, and open space, as represented by parks polygons, trees polygons, empty areas, pedestrian polygons and road surface area polygons. (initial measures showed that cooling system consumption can be reduced by around 15 -20 % with a well-planned building distributions, proper spaces and with using environmental friendly products and building material, temperature levels were also combined in the scenario extracted from satellite images as interpreted from thermal bands 3 times during the period of assessment. Other examples of the assessment of GIS analysis to urban sustainability took place included Waste Productivity, some effects of greenhouse gases measured by the intensity of road polygons and closeness to dwelling areas, industry areas as defined from land use land cover thematic maps produced from classified satellite images then vectors were created to take part in defining their role within the scenarios. City Noise and light intensity assessment was also investigated, as the region experiences rapid development and noise is magnified due to construction activities, closeness of the airports, and highways. The assessment investigated the measures taken by urban planners to reduce degradation or properly manage it. Finally as a conclusion tables were presented to reflect the scenario results in combination with GIS data types, analysis types, and the level of GIS data reliability to measure the sustainability level of a city related to cultural and regional demands.

Birthweight and childhood cancer : preliminary findings from the International Childhood Cancer Cohort Consortium (I4C)

BACKGROUND: Evidence relating childhood cancer to high birthweight is derived primarily from registry and case-control studies. We aimed to investigate this association, exploring the potential modifying roles of age at diagnosis and maternal anthropometrics, using prospectively collected data from the International Childhood Cancer Cohort Consortium.

METHODS: We pooled data on infant and parental characteristics and cancer incidence from six geographically and temporally diverse member cohorts [the Avon Longitudinal Study of Parents and Children (UK), the Collaborative Perinatal Project (USA), the Danish National Birth Cohort (Denmark), the Jerusalem Perinatal Study (Israel), the Norwegian Mother and Child Cohort Study (Norway), and the Tasmanian Infant Health Survey (Australia)]. Birthweight metrics included a continuous measure, deciles, and categories (≥4.0 vs. <4.0 kilogram). Childhood cancer (377 cases diagnosed prior to age 15 years) risk was analysed by type (all sites, leukaemia, acute lymphoblastic leukaemia, and non-leukaemia) and age at diagnosis. We estimated hazard ratios (HR) and 95% confidence intervals (CI) from Cox proportional hazards models stratified by cohort.

RESULTS: A linear relationship was noted for each kilogram increment in birthweight adjusted for gender and gestational age for all cancers [HR = 1.26; 95% CI 1.02, 1.54]. Similar trends were observed for leukaemia. There were no significant interactions with maternal pre-pregnancy overweight or pregnancy weight gain. Birthweight ≥4.0 kg was associated with non-leukaemia cancer among children diagnosed at age ≥3 years [HR = 1.62; 95% CI 1.06, 2.46], but not at younger ages [HR = 0.7; 95% CI 0.45, 1.24, P for difference = 0.02].

CONCLUSION: Childhood cancer incidence rises with increasing birthweight. In older children, cancers other than leukaemia are particularly related to high birthweight. Maternal adiposity, currently widespread, was not demonstrated to substantially modify these associations. Common factors underlying foetal growth and carcinogenesis need to be further explored.

Nonparametric rank tests for non-stationary panels

We develop a set of nonparametric rank tests for non-stationary panels based on multivariate variance ratios which use untruncated kernels. As such, the tests do not require the choice of tuning parameters associated with bandwidth or lag length and also do not require choices with respect to numbers of common factors. The tests allow for unrestricted cross-sectional dependence and dynamic heterogeneity among the units of the panel, provided simply that a joint functional central limit theorem holds for the panel of differenced series. We provide a discussion of the relationships between our setting and the settings for which first- and second generation panel unit root tests are designed. In Monte Carlo simulations we illustrate the small-sample performance of our tests when they are used as panel unit root tests under the more restrictive DGPs for which panel unit root tests are typically designed, and for more general DGPs we also compare the small-sample performance of our nonparametric tests to parametric rank tests. Finally, we provide an empirical illustration by testing for income convergence among countries.

The power of PANIC

The current paper considers the asymptotic local power of second-generation panel unit root tests that are robust to the presence of cross-section dependence in the form of common factors. As a basis for our analysis, we take the PANIC approach of Bai and Ng (2004, 2010), which is one of the single most popular and general second-generation approaches around.

Panicca: panic on cross-section averages

The cross-section average (CA) augmentation approach of Pesaran (2007) and Pesaran et al. (2013), and the principal components-based panel analysis of non-stationarity in idiosyncratic and common components (PANIC) of Bai and Ng (2004, 2010) are among the most popular “second-generation” approaches for cross-section correlated panels. One feature of these approaches is that they have different strengths and weaknesses. The purpose of the current paper is to develop PANICCA, a combined approach that exploits the strengths of both CA and PANIC.

Testing for convergence in carbon dioxide emissions using a century of panel data

This paper tests the convergence in per-capita carbon dioxide emissions for a collection of developed and developing countries using data spanning the period 1870-2002. For this purpose, three recently developed panel unit root tests that permit for dependence among the individual countries are employed. The results lend strong support in favor of convergence for the panel as a whole. Estimates of the speed of this convergence is also provided. © 2007 Springer Science+Business Media B.V.

On the estimation and testing of predictive panel regressions

Hjalmarsson (2010) considers an OLS-based estimator of predictive panel regressions that is argued to be mixed normal under very general conditions. In a recent paper, Westerlund et al. (2016) show that while consistent, the estimator is generally not mixed normal, which invalidates standard normal and chi-squared inference. The purpose of the present paper is to study the consequences of this theoretical result in small samples, which is done using both simulated and real data.