Interaction of visual and haptic information in simulated environments: texture perception

This paper describes experiments relating to the perception of the roughness of simulated surfaces via the haptic and visual senses. Subjects used a magnitude estimation technique to judge the roughness of “virtual gratings” presented via a PHANToM haptic interface device, and a standard visual display unit. It was shown that under haptic perception, subjects tended to perceive roughness as decreasing with increased grating period, though this relationship was not always statistically significant. Under visual exploration, the exact relationship between spatial period and perceived roughness was less well defined, though linear regressions provided a reliable approximation to individual subjects’ estimates.

New evidence on the green building rent and price premium

This paper investigates the effect of voluntary eco-certification on the rental and sale prices of US commercial office properties. Hedonic and logistic regressions are used to test whether there are rental and sale price premiums for LEED and Energy Star certified buildings. The results of the hedonic analysis suggest that there is a rental premium of approximately 6% for LEED and Energy Star certification. A sale price premium of approximately 35% was found for 127 price observations involving LEED rated buildings and 31% for 662 buildings involving Energy Star rated buildings. When compared to samples of similar buildings identified by a binomial logistic regression for LEED-certified buildings, the existence of a rent and sales price premium is confirmed albeit with differences regarding the magnitude of the premium. Overall, the results of this study confirm that LEED and Energy Star buildings exhibit higher rental rates and sales prices per square foot controlling for a large number of location- and property-specific factors.

The relative importance sector and regional factors in Italy

The benefits of sector and regional diversification have been well documented in the literature but have not previously been investigated in Italy. In addition, previous studies have used geographically defined regions, rather than economically functional areas, when performing the analysis even though most would argue that it is the economic structure of the area that will lead to differences in demand and hence property performance. This study therefore uses economically defined regions of Italy to test the relative benefits of regional diversification versus sector diversification within the Italian real estate portfolio. To examine this issue we use constrained cross-section regressions the on the sector and regional affiliation of 14 cities in Italy to extract the “pure” return effects of the different factors using annual data over the period 1989 to 2003. In contrast, to previous studies we find that regional factors effects in Italy have a much greater influence on property returns than sector-specific effects, which is probably a direct result of using the extremely diverse economic regions of Italy rather than arbitrary geographically locations. Be that as it may, the results strongly suggest that that diversification across the regions of Italy used here is likely to offer larger risk reduction benefits than a sector diversification strategy within a region. In other words, fund managers in Italy must monitor the regional composition of their portfolios more closely than its sector allocation. Additionally, the results supports that contemporary position that ‘regional areas’ based on economic function, provide greater diversification benefits rather than areas defined by geographical location.

Attitudinal and demographic determinants of diet quality and implications for policy targeting

Background:  Poor diet quality is a major public health concern that has prompted governments to introduce a range of measures to promote healthy eating. For these measures to be effective, they should target segments of the population with messages relevant to their needs, aspirations and circumstances. The present study investigates the extent to which attitudes and constraints influence healthy eating, as well as how these vary by demographic characteristics of the UK population. It further considers how such information may be used in segmented diet and health policy messages. Methods:  A survey of 250 UK adults elicited information on conformity to dietary guidelines, attitudes towards healthy eating, constraints to healthy eating and demographic characteristics. Ordered logit regressions were estimated to determine the importance of attitudes and constraints in determining how closely respondents follow healthy eating guidelines. Further regressions explored the demographic characteristics associated with the attitudinal and constraint variables. Results:  People who attach high importance to their own health and appearance eat more healthily than those who do not. Risk-averse people and those able to resist temptation also eat more healthily. Shortage of time is considered an important barrier to healthy eating, although the cost of a healthy diet is not. These variables are associated with a number of demographic characteristics of the population; for example, young adults are more motivated to eat healthily by concerns over their appearance than their health. Conclusions:  The approach employed in the present study could be used to inform future healthy eating campaigns. For example, messages to encourage the young to eat more healthily could focus on the impact of diets on their appearance rather than health.

Eco-labeling, rents, sales prices and occupancy rates: do LEED and Energy Star labeled offices obtain multiple premiums?

Drawing upon an updated and expanded dataset of Energy Star and LEED labeled commercial offices, this paper investigates the effect of eco-labeling on rental rates, sale prices and occupancy rates. Using OLS and robust regression procedures, hedonic modeling is used to test whether the presence of an eco-label has a significant positive effect on rental rates, sale prices and occupancy rates. The study suggests that estimated coefficients can be sensitive to outlier treatment. For sale prices and occupancy rates, there are notable differences between estimated coefficients for OLS and robust regressions. The results suggest that both Energy Star and LEED offices obtain rental premiums of approximately 3%. A 17% sale price premium is estimated for Energy Star labeled offices but no significant sale price premium is estimated for LEED labeled offices. Surprisingly, no significant occupancy premium is estimated for Energy Star labeled offices and a negative occupancy premium is estimated for LEED labeled offices.

Comment on ``Conditional Exceedance Probabilities''

In a recent paper, Mason et al. propose a reliability test of ensemble forecasts for a continuous, scalar verification. As noted in the paper, the test relies on a very specific interpretation of ensembles, namely, that the ensemble members represent quantiles of some underlying distribution. This quantile interpretation is not the only interpretation of ensembles, another popular one being the Monte Carlo interpretation. Mason et al. suggest estimating the quantiles in this situation; however, this approach is fundamentally flawed. Errors in the quantile estimates are not independent of the exceedance events, and consequently the conditional exceedance probabilities (CEP) curves are not constant, which is a fundamental assumption of the test. The test would reject reliable forecasts with probability much higher than the test size.

Evaluating raw ensembles with the continuous ranked probability score

The continuous ranked probability score (CRPS) is a frequently used scoring rule. In contrast with many other scoring rules, the CRPS evaluates cumulative distribution functions. An ensemble of forecasts can easily be converted into a piecewise constant cumulative distribution function with steps at the ensemble members. This renders the CRPS a convenient scoring rule for the evaluation of ‘raw’ ensembles, obviating the need for sophisticated ensemble model output statistics or dressing methods prior to evaluation. In this article, a relation between the CRPS score and the quantile score is established. The evaluation of ‘raw’ ensembles using the CRPS is discussed in this light. It is shown that latent in this evaluation is an interpretation of the ensemble as quantiles but with non-uniform levels. This needs to be taken into account if the ensemble is evaluated further, for example with rank histograms.

The use of land-sea warming contrast under climate change to improve impact metrics

A favoured method of assimilating information from state-of-the-art climate models into integrated assessment models of climate impacts is to use the transient climate response (TCR) of the climate models as an input, sometimes accompanied by a pattern matching approach to provide spatial information. More recent approaches to the problem use TCR with another independent piece of climate model output: the land-sea surface warming ratio (φ). In this paper we show why the use of φ in addition to TCR has such utility. Multiple linear regressions of surface temperature change onto TCR and φ in 22 climate models from the CMIP3 multi-model database show that the inclusion of φ explains a much greater fraction of the inter-model variance than using TCR alone. The improvement is particularly pronounced in North America and Eurasia in the boreal summer season, and in the Amazon all year round. The use of φ as the second metric is beneficial for three reasons: firstly it is uncorrelated with TCR in state-of-the-art climate models and can therefore be considered as an independent metric; secondly, because of its projected time-invariance, the magnitude of φ is better constrained than TCR in the immediate future; thirdly, the use of two variables is much simpler than approaches such as pattern scaling from climate models. Finally we show how using the latest estimates of φ from climate models with a mean value of 1.6—as opposed to previously reported values of 1.4—can significantly increase the mean time-integrated discounted damage projections in a state-of-the-art integrated assessment model by about 15 %. When compared to damages calculated without the inclusion of the land-sea warming ratio, this figure rises to 65 %, equivalent to almost 200 trillion dollars over 200 years.

Regional performance and characteristics of Indian manufacturing industry

This paper investigates the regional characteristics of Indian manufacturing industry. Its aim is to assess whether geography plays any major role in determining the performance or characteristics of Indian manufacturing firms, and in order to do this, it presents the results of cross-section regressions estimated on the basis of a balanced sample of 1607 firms across the 30 Indian states. The results suggest that firm performance and characteristics are related to many of the expected industrial organization variables. However, there is also evidence of significant region–state influences on both the performance and characteristics of Indian manufacturing industry. As such, the results demonstrate that analyses which focus solely on standard non-spatial industrial organization variables will fail to explain much of the cross-sectional variation in firm performance and characteristics. In particular, while there are no systematic simple centre–periphery variations in the Indian regional economic system, there is evidence to suggest that industrial spatial concentration, regional specialization, and regional market size play a key role in determining the performance and characteristics of Indian manufacturing industry.

Structure, conduct and the stochastic volatility of food markets: theory and empirics

The relationship between price volatility and competition is examined. Atheoretic, vector auto regressions on farm prices of wheat and retail prices of derivatives (flour, bread, pasta, bulgur and cookies) are compared to results from a dynamic, simultaneous-equations model with theory-based farm-to-retail linkages. Analytical results yield insights about numbers of firms and their impacts on demand- and supply-side multipliers, but the applications to Turkish time series (1988:1-1996:12) yield mixed results.

Conditioning model output statistics of regional climate model precipitation on circulation patterns

Dynamical downscaling of Global Climate Models (GCMs) through regional climate models (RCMs) potentially improves the usability of the output for hydrological impact studies. However, a further downscaling or interpolation of precipitation from RCMs is often needed to match the precipitation characteristics at the local scale. This study analysed three Model Output Statistics (MOS) techniques to adjust RCM precipitation; (1) a simple direct method (DM), (2) quantile-quantile mapping (QM) and (3) a distribution-based scaling (DBS) approach. The modelled precipitation was daily means from 16 RCMs driven by ERA40 reanalysis data over the 1961–2000 provided by the ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts) project over a small catchment located in the Midlands, UK. All methods were conditioned on the entire time series, separate months and using an objective classification of Lamb's weather types. The performance of the MOS techniques were assessed regarding temporal and spatial characteristics of the precipitation fields, as well as modelled runoff using the HBV rainfall-runoff model. The results indicate that the DBS conditioned on classification patterns performed better than the other methods, however an ensemble approach in terms of both climate models and downscaling methods is recommended to account for uncertainties in the MOS methods.

Rural-urban disparities in child nutrition in Bangladesh and Nepal

Background The persistence of rural-urban disparities in child nutrition outcomes in developing countries alongside rapid urbanisation and increasing incidence of child malnutrition in urban areas raises an important health policy question - whether fundamentally different nutrition policies and interventions are required in rural and urban areas. Addressing this question requires an enhanced understanding of the main drivers of rural-urban disparities in child nutrition outcomes especially for the vulnerable segments of the population. This study applies recently developed statistical methods to quantify the contribution of different socio-economic determinants to rural-urban differences in child nutrition outcomes in two South Asian countries – Bangladesh and Nepal. Methods Using DHS data sets for Bangladesh and Nepal, we apply quantile regression-based counterfactual decomposition methods to quantify the contribution of (1) the differences in levels of socio-economic determinants (covariate effects) and (2) the differences in the strength of association between socio-economic determinants and child nutrition outcomes (co-efficient effects) to the observed rural-urban disparities in child HAZ scores. The methodology employed in the study allows the covariate and coefficient effects to vary across entire distribution of child nutrition outcomes. This is particularly useful in providing specific insights into factors influencing rural-urban disparities at the lower tails of child HAZ score distributions. It also helps assess the importance of individual determinants and how they vary across the distribution of HAZ scores. Results There are no fundamental differences in the characteristics that determine child nutrition outcomes in urban and rural areas. Differences in the levels of a limited number of socio-economic characteristics – maternal education, spouse’s education and the wealth index (incorporating household asset ownership and access to drinking water and sanitation) contribute a major share of rural-urban disparities in the lowest quantiles of child nutrition outcomes. Differences in the strength of association between socio-economic characteristics and child nutrition outcomes account for less than a quarter of rural-urban disparities at the lower end of the HAZ score distribution. Conclusions Public health interventions aimed at overcoming rural-urban disparities in child nutrition outcomes need to focus principally on bridging gaps in socio-economic endowments of rural and urban households and improving the quality of rural infrastructure. Improving child nutrition outcomes in developing countries does not call for fundamentally different approaches to public health interventions in rural and urban areas.

Reconstruction and prediction of variations in the open solar magnetic flux and interplanetary conditions

Historic geomagnetic activity observations have been used to reveal centennial variations in the open solar flux and the near-Earth heliospheric conditions (the interplanetary magnetic field and the solar wind speed). The various methods are in very good agreement for the past 135 years when there were sufficient reliable magnetic observatories in operation to eliminate problems due to site-specific errors and calibration drifts. This review underlines the physical principles that allow these reconstructions to be made, as well as the details of the various algorithms employed and the results obtained. Discussion is included of: the importance of the averaging timescale; the key differences between “range” and “interdiurnal variability” geomagnetic data; the need to distinguish source field sector structure from heliospherically-imposed field structure; the importance of ensuring that regressions used are statistically robust; and uncertainty analysis. The reconstructions are exceedingly useful as they provide calibration between the in-situ spacecraft measurements from the past five decades and the millennial records of heliospheric behaviour deduced from measured abundances of cosmogenic radionuclides found in terrestrial reservoirs. Continuity of open solar flux, using sunspot number to quantify the emergence rate, is the basis of a number of models that have been very successful in reproducing the variation derived from geomagnetic activity. These models allow us to extend the reconstructions back to before the development of the magnetometer and to cover the Maunder minimum. Allied to the radionuclide data, the models are revealing much about how the Sun and heliosphere behaved outside of grand solar maxima and are providing a means of predicting how solar activity is likely to evolve now that the recent grand maximum (that had prevailed throughout the space age) has come to an end.

Variability and trends in stratospheric NO2 in Antarctic summer, and implications for stratospheric NOy

NO2 measurements during 1990–2007, obtained from a zenith-sky spectrometer in the Antarctic, are analysed to determine the long-term changes in NO2. An atmospheric photochemical box model and a radiative transfer model are used to improve the accuracy of determination of the vertical columns from the slant column measurements, and to deduce the amount of NOy from NO2. We find that the NO2 and NOy columns in midsummer have large inter-annual variability superimposed on a broad maximum in 2000, with little or no overall trend over the full time period. These changes are robust to a variety of alternative settings when determining vertical columns from slant columns or determining NOy from NO2. They may signify similar changes in speed of the Brewer-Dobson circulation but with opposite sign, i.e. a broad minimum around 2000. Multiple regressions show significant correlation with solar and quasi-biennial-oscillation indices, and weak correlation with El Nino, but no significant overall trend, corresponding to an increase in Brewer-Dobson circulation of 1.4±3.5%/decade. There remains an unexplained cycle of amplitude and period at least 15% and 17 years, with minimum speed in about 2000.

Broker use and the cost of liquidity in commercial real estate investment

Purpose – Commercial real estate is a highly specific asset: heterogeneous, indivisible and with less information transparency than most other commonly held investment assets. These attributes encourage the use of intermediaries during asset acquisition and disposal. However, there are few attempts to explain the use of different brokerage models (with differing costs) in different markets. This study aims to address this gap. Design/methodology/approach – The study analyses 9,338 real estate transactions in London and New York City from 2001 to 2011. Data are provided by Real Capital Analytics and cover over $450 billion of investments in this period. Brokerage trends in the two cities are compared and probit regressions are used to test whether the decision to transact with broker representation varies with investor or asset characteristics. Findings – Results indicate greater use of brokerage in London, especially by purchasers. This persists when data are disaggregated by sector, time or investor type, pointing to the role of local market culture and institutions in shaping brokerage models and transaction costs. Within each city, the nature of the investors involved seems to be a more significant influence on broker use than the characteristics of the assets being traded. Originality/value – Brokerage costs are the single largest non-tax charge to an investor when trading commercial real estate, yet there is little research in this area. This study examines the role of brokers and provides empirical evidence on factors that influence the use and mode of brokerage in two major investment destinations.