Different Levels Of Aggregation Research Articles

Benchmarking a triplet of official estimates

The publication of official statistics at different levels of aggregation requires a benchmarking step. Difficulties arise when a benchmarking method needs to be applied to a triplet of related estimates, at multiple stages of aggregation. For ratios of totals, external benchmarking constraints for the triplet (numerator, denominator, ratio) are that the weighted sum of denominator/numerator/ratio estimates equals to a constant. The benchmarking weight, applied to the ratio estimates, is a function of the denominator estimates. For example, the United States Department of Agriculture’s National Agricultural Statistics Service’s county-level, end-of-season acreage, production and yield estimates need to aggregate to the corresponding agricultural statistics district-level estimates, which also need to aggregate to the corresponding prepublished state-level values. Moreover, the definition of yield, as the ratio of production to harvested acreage, needs to hold at the county level, the agricultural statistics district level and the state level. We discuss different methods of applying benchmarking constraints to a triplet (numerator, denominator, ratio), at multiple stages of aggregation, where the denominator and the ratio are modeled and the numerator is derived. County-level and agricultural statistics district-level, end-of-season acreage, production and yield estimates are constructed and compared using the different methods. Results are illustrated for 2014 corn and soybean in Indiana, Iowa and Illinois.

Improving Patients' Choice of Clinician by Including Roll-up Measures in Public Healthcare Quality Reports: an Online Experiment.

Public reports on healthcare quality typically include complex data. To lower the cognitive burden of interpreting these data, some report designers create summary, or roll-up, measures combining multiple indicators of quality into one score. Little is known about how the availability of roll-ups affects clinician choice. To determine how presenting quality scores at different levels of aggregation affects patients' clinician choices. We conducted a simulated clinician-choice experiment, randomizing participants to three versions of a public reporting website and comparing their clinician choices. One version aggregated all clinician-level quality measures into roll-ups, the second provided disaggregated (drill-down) scores only, and the third offered both roll-ups and drill-downs. Five hundred fifty panelists drawn from a probability-based Internet panel. We assessed the amount of effort participants exerted by tracking the length of time spent on the website and the number of concrete actions taken on the website (e.g., clicking items). We evaluated decision quality by measuring whether participants selected a clinician who performed more poorly than others and incongruence between participants' stated preferences for dimensions of quality and their chosen clinician's performance on those dimensions. Participants seeing drill-downs alone (mean = 14.9) or with roll-ups (mean = 19.2) took more actions than those who saw roll-ups alone (mean = 10.5) (ps < 0.05). However, participants seeing only drill-downs made poorer choices than those who saw roll-ups alone or with drill-downs. More participants seeing drill-downs chose a clinician who was outperformed (36.3% versus 23.4% [roll-up] and 25.6% [drill-down + roll-up], ps < 0.05) and made choices incongruent with stated preferences (51.2% versus 45.6% [roll-up] and 47.5% [drill-down + roll-up], ps < 0.05). The distinction between roll-up and drill-down was somewhat stronger for sicker participants. Our results suggest that roll-ups in healthcare quality reports, alone or as a complement to drill-downs, can help patients make better decisions for themselves.

PILOT PRODUCTION ENVIRONMENTS DRIVEN BY DIGITAL TWINS

Establishing production environments requires joint decision-making by stakeholders from many disciplines at different levels of aggregation, because of the significant, unpredictable, and risky investments involved. Pilot plants offer a platform to develop, test, improve, and upscale (parts of) a production environment. They consist of physical and virtual components that are integrated, based on the digital twin concept. This concept synthesises sensoring/measurement ( in situ and ex situ ) with the modelling and simulation of existing and evolving resources/processes at operational, tactical, and strategic levels. Virtual dashboards supplement the pilot plant to provide an insightful basis for decision-making for all the perspectives and stakeholders involved.

The Value of the Lens Model Paradigm in Neuropsychological Assessment

Abstract. A key issue of neuropsychological assessment is how to optimally associate mental function with neurobiological function. Correspondingly, clinical neuropsychology explores the relationship between mental dysfunction and neuropathology. Here, the well-known Brunswik-lens paradigm of psychological assessment is adapted to neuropsychology and a “neuro-lens model” (NLM) is suggested to better cross-link psychological and neurobiological construct domains. NLMs may contribute to optimizing coordinated measurement at different levels of aggregation and to clarifying issues of reliability and construct validation. Although the potential value of NLMs is challenged by complex implementation and analysis, Brunswik-type NLM modeling might be a promising new approach to clinical decision-making and assessment, test development, and research.

Minding Your Ps and Qs: Going from Micro to Macro in Measuring Prices and Quantities

Key macro indicators such as output, productivity and inflation are based on a complex system of collection from different samples and different levels of aggregation across multiple statistical agencies. The Census Bureau collects nominal sales, the Bureau of Labor Statistics collects prices, and the Bureau of Economic Analysis constructs nominal and real GDP using these and other data sources. The price and quantity data are integrated at a high level of aggregation (product and industry classes). A similar mismatch of price and nominal variables pervades the productivity data, which use industry-level producer price indexes as deflators. This paper explores alternative methods for re-engineering key national output and price indices using transactions-level data. Such re-engineering offers the promise of greatly improved macroeconomic data along many dimensions. First, price and quantity would be based on the same observations. Second, the granularity of data could be greatly increased on many dimensions. Third, time series could be constructed at a higher frequency and on a more timely basis. Fourth, the use of transactions-level data opens the door to new methods for tracking product turnover and other sources of product quality change that may be biasing the key national indicators. Implementing such a new architecture for measuring economic activity and price change poses considerable challenges. This paper explores these challenges, along with a re-engineered approach’s implications for the biases in the traditional approaches to measuring output growth, productivity growth, and inflation.

Supply chain decision support systems based on a novel hierarchical forecasting approach

Time series forecasting plays an important role in many decision support systems, also in those related to the management of supply chains. Forecast accuracy is, therefore, essential to optimise the efficiency of any supply chain. One aspect that is often overlooked is the fact that sales of many products within an organization are assembled as complex hierarchies with different levels of aggregation. Very often forecasts are produced regardless of such structure, though forecasting accuracy may be improved by taking it into account. In this paper, an approach for hierarchical time series forecasting based on State Space modelling is proposed. Previous developments provide solutions to the hierarchical forecasting problem by algebra manipulations based on forecasts produced by independent models for each time series involved in the hierarchy. The solutions produce optimal reconciled forecasts for each individual forecast horizon, but the link along time that is implied by the dynamics of the models is completely ignored. Therefore, the novel approach in this paper improves upon past research at least in two key points. Firstly, the algebra is already encoded in the State Space system and the Kalman Filter algorithm, giving an elegant and clean solution to the problem. Secondly, the State Space approach is optimal both across the hierarchy, as expected, but also along time, something missing in past developments. The approach is assessed by comparing its forecasting performance to the existing methods, through simulations and using real data of a Spanish grocery retailer.

Detecting periods of exuberance: A look at the role of aggregation with an application to house prices

The recently developed SADF and GSADF unit root tests of Phillips and Yu (2011) and Phillips et al. (2015a,b) have become popular in the literature for detecting exuberance in asset prices. In this paper, we examine through simulation experiments the effect of cross-sectional aggregation on the power properties of these tests. The simulation design considered is based on simulated data and actual housing data for both U.S. metropolitan areas and international housing markets and thus allows us to draw conclusions for different levels of aggregation. Our findings suggest that aggregation lowers the power of both the SADF and GSADF tests. The effect, however, is much larger for the SADF test. We also provide evidence that tests based on panel data techniques, namely the panel GSADF test recently proposed by Pavlidis et al. (2016), can perform substantially better than univariate tests applied to aggregated series. Furthermore, we also illustrate the date-stamping procedure under the univariate/panel GSADF procedure uncovering novel evidence on the role of interest rates and policy uncertainty as factors explaining episodes of widespread mildly explosive dynamics in housing markets.

A general approach to module-based plant design

Increasing economic challenges lead to the need for faster plant design in process industry. In this context, a promising approach is module-based plant design. Thereby, for the accomplishment of the required design tasks modules are selected from databases and configured instead of time-consuming and tailor-made plant design.Within this work, a general approach to module-based plant design is introduced and illustrated based on an example. To structure the design procedure different types of modules are defined for different design tasks: PFD, P&ID, Equipment and 3D Layout. Additionally, module selection can be performed at different Levels of Aggregation to cope with the high variance of applications. This enables module selection and configuration avoiding time-consuming, tailored modifications. Since modules are unmodifiable and project-independent, the module-based plant design approach presented is based on a consistent module definition. This work provides a framework to integrate some of the existing modularization approaches into a general module-based plant design approach. However, most often new approaches are necessary to accomplish the design tasks within the presented module-based plant design approach. Thus, this work also identifies open research gaps that need to be filled by future research.

Detecting Periods of Exuberance: A Look at the Role of Aggregation with an Application to House Prices

The recently developed SADF and GSADF unit root tests of Phillips et al. (2011) and Phillips et al. (2015) have become popular in the literature for detecting exuberance in asset prices. In this paper, we examine through simulation experiments the effect of cross-sectional aggregation on the power properties of these tests. The simulation design considered is based on actual housing data for both U.S. metropolitan and international housing markets and thus allows us to draw conclusions for different levels of aggregation. Our findings suggest that aggregation lowers the power of both the SADF and GSADF tests. The effect, however, is much larger for the SADF test. We also provide evidence that tests based on panel data techniques, namely the panel GSADF test recently proposed by Pavlidis et al. (2015), can perform substantially better than univariate tests applied to aggregated series.

Impact of ConcanavalinA affinity in the intracellular fate of Protein Corona on Glucosamine Au nanoparticles

Biological fate and toxicity of nanoparticles (NPs) are connected to the interaction between NPs and the protein corona (PC) spontaneously forming around NPs in biological matrixes. PC is a dynamic entity that confers biological identity to NPs. In this work, fluorescence cross-correlation spectroscopy (FCCS) is used to study the impact of specific interactions between the NP surface and proteins on the intracellular fate of PC. The stability of the PC formed around glucosamide-functionalized Au-NPs from ConcanavalinA (ConA) or Bovine Serum Albumin (BSA) is characterized by FCCS. The NPs show higher affinity for ConA and competitive assays show that ConA easily exchanges BSA. A549 cells are exposed to glucosamide-functionalized Au-NPs with preformed ConA and BSA PCs. Intracellularly the frequency of cross-correlation for Au NPs with ConA PC remains constant to a 70% value until 24 h while for BSA it decreases to a 15% during the same period. FCCS measurements in several locations in the cell point out a different level of aggregation for the NPs with either ConA or BSA PCs. Our results show that the affinity of NPs functionalized with a ligand with affinity for a specific protein in bulk is retained intracellularly influencing NP fate and translocation.

Stakeholder Specific Multi-Scale Spatial Representation of Urban Building-Stocks

Urban building-stocks use a significant amount of resources and energy. At the same time, they have a large potential for energy efficiency measures (EEM). To support decision-making and planning, spatial building-stock models are used to examine the current state and future development of urban building-stocks. While these models normally focus on specific cities, generic and broad stakeholder groups such as planners and policy makers are often targeted. Consequently, the visualization and communication of results are not tailored to these stakeholders. The aim of this paper is to explore the possibilities of mapping and representing energy use of urban building-stocks at different levels of aggregation and spatial distributions, to communicate with specific stakeholders involved in the urban development process. This paper uses a differentiated building-stock description based on building-specific data and measured energy use from energy performance certificates for multi-family buildings (MFB) in the city of Gothenburg. The building-stock description treats every building as unique, allowing results to be provided at any level of aggregation to suit the needs of the specific stakeholders involved. Calculated energy use of the existing stock is within 10% of the measured energy use. The potential for EEM in the existing stock is negated by the increased energy use due to new construction until 2035, using a development scenario based on current renovation rates and planned developments. Visualizations of the current energy use of the stock as well as the impact of renovation and new construction are provided, targeting specific local stakeholders.

Early stage SME bankruptcy: does the local banking market matter?

This paper investigates the role of local context, with regard to the effect of local financial development and banking concentration, on a firm’s probability of bankruptcy at the post-creation stage. Our empirical setting is based on the logit multilevel model that better allows the treatment of data referring to different levels of aggregation (firm and local variables) applied to companies located in Italy. We find that a higher level of financial development in a province decreases the likelihood of corporate bankruptcy. This result is robust considering a 2SLS regression in which we use instruments for the local financial development and for the concentration of bank branches. In addition, our estimations suggest that the effect of local financial development and bank concentration is shaped by size. Local financial development is particularly significant for small firms, which traditionally suffer from great difficulty in accessing credit, whereas local banking concentration reduces the probability of bankruptcy for medium-sized firms.

Visual and Colorimetric High-Throughput Analysis of Chiral Carboxylic Acids Based on Enantioselective Charge Shielding of Gold Nanoparticles.

Because chiral carboxylic acids (CCAs) are a class of important biological molecules and common functional moieties found in pharmaceutical molecules, the chiral analysis of CCAs has received much attention. Herein, we developed a simple, rapid, and cost-effective method for visual and colorimetric high-throughput analysis of CCAs using chiral di-imine structure-modified gold nanoparticles (C-AuNPs) as the probe. The C-AuNPs are positively charged in the presence of zinc ion, and they can be enantioselectively shielded by the negatively charged CCA enantiomers. Therefore, upon the addition of different concentrations and enantiomeric excess (ee) of CCAs, the C-AuNP-based sensor shows the different levels of aggregation along with the visual changes in solution color, which can achieve simultaneous analysis of the concentration and ee of CCAs. The chiral recognition mechanism based on C-AuNPs was investigated by the determination of binding constants ( K) and molecular simulation methods. Our approach is expected to have the wide-ranging applications in the developing region for enantio-sensing of various chiral drugs and biomolecules.

Sectoral Aggregation Error in the Accounting of Energy and Emissions Embodied in Trade and Consumption

SummaryCorrectly accounting for the energy and emissions embodied in consumption and trade is essential to effective climate policy design. Robust methods are needed for both policy making and research—for example, the assignment of border carbon adjustments (BCAs) and greenhouse gas emission reduction responsibilities rely on the consistency and accuracy of such estimates. This analysis investigates the potential magnitude and consequences of the error present in estimates of energy and emissions embodied in trade and consumption. To quantify the error of embodied emissions accounting, we compare the results from the disaggregated Global Trade Analysis Project (GTAP 8) data set, which contains 57 sectors to results from different levels of aggregation of this data set (3, 7, 16, and 26 sectors), using 5,000 randomly generated sectoral aggregation schemes as well as aggregations generated using several commonly applied decisions rules. We find that some commonly applied decision rules for sectoral aggregation can produce a large error. We further show that an aggregation scheme that clusters sectors according to their energy, emissions, and trade intensities (net exports over output) can minimize error in embodied energy and emissions accounting at different levels of aggregation. This sectoral aggregation scheme can be readily used in any input‐output analysis and provide useful information for computable general equilibrium modeling exercises in which sector aggregation is necessary, although our findings suggest that, when possible, the most disaggregated data available should be used.

Towards a theory of disintegration

ABSTRACTExisting theories of European integration offer little purchase on the problems facing the European Union today. New theories of disintegration are emerging, but they remain disjointed. The purpose of this comment is to suggest an overarching theoretical framework. This framework helps to structure the existing literature and to suggest new areas for research. It also helps to explain how integration and disintegration interact at different levels of aggregation.

Attitudes and mode choice: Measurement and evaluation of interrelation

Abstract The study is embedded in an evaluation of a travel awareness campaign for children. According to the research design, two attitude and travel surveys were conducted with an intervention in-between. The analysis is based on the before-survey, which served as methodological experiment measuring attitudes at different levels of aggregation: (A) basic personal values, (B) one-dimensional (unspecific) attitudes as typically used in TPB models, and (C) multi-dimensional (content-related) attitudes towards travel modes. The relationships of all groups with intention and behaviour are examined. The results show that one-dimensional attitudes (B) have the highest explanatory power for travel-related intentions and behaviour.

Bipartisanship Breakdown, Functional Networks, and Forensic Analysis in Spanish 2015 and 2016 National Elections

We present a social network and forensic analysis of the vote counts of Spanish national elections that took place in December 2015 and their sequel in June 2016. We initially consider the phenomenon of bipartisanship breakdown by analyzing spatial distributions of several bipartisanship indices. We find that such breakdown is more prominently close to cosmopolite and largely populated areas and less important in rural areas where bipartisanship still prevails, and its evolution mildly consolidates in the 2016 round, with some evidence of bipartisanship reinforcement which we hypothesize to be due to psychological mechanisms of risk aversion. Subsequently, a functional network analysis detects an effective partition of municipalities which remarkably coincides with the first-level political and administrative division of autonomous communities. Finally, we explore to which extent vote data are faithful by applying forensic techniques to vote statistics. Results based on deviation from Benford’s law are mixed and vary across different levels of aggregation. As a complementary metric, we further explore the cooccurring statistics of vote share and turnout, finding a mild tendency in the clusters of the conservative party to smear out towards the area of high turnout and vote share, what has been previously interpreted as a possible sign of incremental fraud.

Corporate competitiveness and sustainability risks

This paper aims at providing a theoretical analysis of the existing research on corporate competition and sustainability risks that occur when companies aspire to reach maximum competitive advantages and gain competitive benefits compared to their rivals. Competitiveness has been described as a multidimensional, theoretical and relative concept linked with the market mechanism. The concept of competitiveness may refer to different levels of aggregation: national, regional, industrial and individual companies. This paper contributes to the theoretical research on corporate competitiveness by the analysis of old and new definitions of this category. It also notes that the sustainability risks connected to competition can be divided into several groups where the authors highlight environmental, legal, financial risks, behaviour risks and state-related risks as the most crucial ones. For companies to be fit for the competitive challenge, the paper identifies main characteristics of such risks and gives policy guidance for their avoidance.

Dinámica del sistema migratorio argentino-paraguayo

Asentada originalmente en zonas de frontera común y del litoral argentino, la migración paraguaya a este país se funde en la fase de industrialización de mediados del siglo XX que activa el ciclo de migración interna desde las provincias al Área Metropolitana de Buenos Aires, en simultáneo con la guerra civil de 1947 y la dictadura stronista, impulsando un proceso emigratorio hacia la Argentina que se sostiene hasta el presente. La estructura económica y sociolaboral paraguaya se ordena en los últimos años conforme a un modelo agrario de exportación de commodities, con inequitativa distribución del ingreso, baja empleabilidad y alto subempleo. El tipo de inserción laboral existente en ese país es replicado por la diáspora en Argentina, donde el empleo de hombres en rubros como la construcción y de mujeres en casas de familia, por citar algunos casos, reproducen dinámicas de subsistencia análogas a las de los mismos sectores populares del Paraguay. A continuación, ofreceremos un caso desde el cual retomar la discusión sobre la Ciudadanía MERCOSUR. Demostraremos que la existencia de una regionalización de la dinámica migratoria paraguaya de larga data se sobrepone a los particularismos localistas, evidenciando una red de interacciones de migración cuya dinámica está constituida por el funcionamiento de una variedad de redes que unen a actores en los diferentes niveles de agregación y que se despliega desde el Paraguay hasta los grandes centros urbanos de la Argentina, con lógicas propias que, en definitiva, responden a un pasado, a un presente y a un futuro común.

Reference density trends in the major disciplines

Abstract The aim of this study was to determine whether different areas of knowledge presented different behaviour with regard to the number of references cited per journal document or if, conversely, they shared the same reference density practices. Bibliometric and bibliographic data were collected from 27,141 journals (indexed between 2001 and 2015 in the SCImago Journal & Country Rank (SJR)) and the growth rates in reference density and number of documents and journals in each category were calculated at different levels of aggregation. Our analysis identified that (a) mean reference density values in some Social Sciences and Arts and Humanities categories were equal to or higher than those in the “hard sciences”; (b) reference density growth rates in these disciplines were not as high as those in the hard sciences and, in general, did not correspond with growth rates in the number of documents produced; (c) this can be considered an indication that citation-based evaluation practices affect publication habits; and (d) no significant differences were found in mean values or growth rates between Gold Open Access and Non Gold Open Access journals.