On the efficacy of techniques for evaluating multivariate volatility forecasts

The performance of techniques for evaluating multivariate volatility forecasts are not yet as well understood as their univariate counterparts. This paper aims to evaluate the efficacy of a range of traditional statistical-based methods for multivariate forecast evaluation together with methods based on underlying considerations of economic theory. It is found that a statistical-based method based on likelihood theory and an economic loss function based on portfolio variance are the most effective means of identifying optimal forecasts of conditional covariance matrices.

Economic growth and inequality patterns in the presence of costly technology adoption and uncertainty

We develop a stochastic endogenous growth model to explain the diversity in growth and inequality patterns and the non-convergence of incomes in transitional economies where an underdeveloped financial sector imposes an implicit, fixed cost on the diversification of idiosyncratic risk. In the model endogenous growth occurs through physical and human capital deepening, with the latter being the more dominant element. We interpret the fixed cost as a ‘learning by doing’ cost for entrepreneurs who undertake risk in the absence of well developed financial markets and institutions that help diversify such risk. As such, this cost may be interpreted as the implicit returns foregone due to the lack of diversification opportunities that would otherwise have been available, had such institutions been present. The analytical and numerical results of the model suggest three growth outcomes depending on the productivity differences between the projects and the fixed cost associated with the more productive project. We label these outcomes as poverty trap, dual economy and balanced growth. Further analysis of these three outcomes highlights the existence of a diversity within diversity. Specifically, within the ‘poverty trap’ and ‘dual economy’ scenarios growth and inequality patterns differ, depending on the initial conditions. This additional diversity allows the model to capture a richer range of outcomes that are consistent with the empirical experience of several transitional economies.

Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

The probability archive : from essence to uncertainty in the mediation of knowledge

This volume puts together the works of a group of distinguished scholars and active researchers in the field of media and communication studies to reflect upon the past, present, and future of new media research. The chapters examine the implications of new media technologies on everyday life, existing social institutions, and the society at large at various levels of analysis. Macro-level analyses of changing techno-social formation – such as discussions of the rise of surveillance society and the "fifth estate" – are combined with studies on concrete and specific new media phenomena, such as the rise of Pro-Am collaboration and "fan labor" online. In the process, prominent concepts in the field of new media studies, such as social capital, displacement, and convergence, are critically examined, while new theoretical perspectives are proposed and explicated. Reflecting the inter-disciplinary nature of the field of new media studies and communication research in general, the chapters interrogate into the problematic through a range of theoretical and methodological approaches. The book should offer students and researchers who are interested in the social impact of new media both critical reviews of the existing literature and inspirations for developing new research questions.

Provision of customer knowledge to supply chains

Knowledge about customers is vital for supply chains in order to ensure customer satisfaction. In an ideal supply chain environment, supply chain partners are able to perform planning tasks collaboratively, because they share information. However, customers are not always able or willing to share information with their suppliers. End consumers, on the one hand, do not usually provide a retail company with demand information. On the other hand, industrial customers might consciously hide information. Wherever a supply chain is not provided with demand forecast information, it needs to derive these demand forecasts by other means. Customer Relationship Management provides a set of tools to overcome informational uncertainty. We show how CRM and SCM information can be integrated on the conceptual as well as technical levels in order to provide supply chain managers with relevant information.

Asset specificity and behavioral uncertainty as moderators of the sales growth-employment growth relationship in emerging ventures

New venture growth is a central topic in entrepreneurship research. Although sales growth is emerging as the most commonly used measure of growth for emerging ventures, employment growth has also been used frequently. However, empirical research demonstrates that there are only very low to moderately sized correlations between the two (Delmar et aL, 2003; Weinzimmer, et al., 1998). In addition) sales growth and employment growth respond differently to a wide variety of criteria (Baum et al., 2001; Delmar et al., 2003). In this study we use transaction cost economics (Williamson, 1996) as a theoretical base to examine transaction cost influences on the addition of new employees as emerging ventures experience sales growth. \\le theorize that transaction cost economics variables will moderate the relationship between sales growth and employment growth. W'e develop and test hypotheses related to asset specificity, behavioral uncertainty, and the influence of resource munificence on the strength of the sales growth/ employment growth relationship. Asset specificity is theorized to be a positive moderator of the relationship between sales growth and employment growth. When the behavioral uncertainty associated with adding new employees is greater than that of outsourcing or subcontracting, it is hypothesized to be a negative moderator of the sales growth/employment growth relationship. We also hypothesize that resource scarcity will strengthen those relationships.

The effect of project information on the accuracy of building price forecasts

A fundamental proposition is that the accuracy of the designer's tender price forecasts is positively correlated with the amount of information available for that project. The paper describes an empirical study of the effects of the quantity of information available on practicing Quantity Surveyors' forecasting accuracy. The methodology involved the surveyors repeatedly revising tender price forecasts on receipt of chunks of project information. Each of twelve surveyors undertook two projects and selected information chunks from a total of sixteen information types. The analysis indicated marked differences in accuracy between different project types and experts/non-experts. The expert surveyors' forecasts were not found to be significantly improved by information other than that of basic building type and size, even after eliminating project type effects. The expert surveyors' forecasts based on the knowledge of building type and size alone were, however, found to be of similar accuracy to that of average practitioners pricing full bills of quantities.

Gender-diverse boards and properties of analyst earnings forecasts

Using a sample of 2,200 US listed firm year observations (2001-2007)this study shows a positive (negative) relation between female participation in corporate boards and analysts' earnings forecast accuracy (dispersion), after controlling for earnings quality, corporate governance, audit quality, stock price informativeness and potential endogeneity. Our findings are important as they suggest that board diversity adds to the transparency and accuracy of financial reports such that earnings expectations are likely to be more accurate for these firms.

Perceptions of land-use uncertainty in Queensland's resource-based regions

Some of Queensland's regions are experiencing rapid changes related to the recent and growing capacity to more effectively exploit significant energy sources. These changes have triggered land-use conflicts between the mining sector and other economic sectors, mainly agriculture. These conflicts fuel existing uncertainty surrounding the current and future economic, social and environmental impacts of extractive industries. This paper explores the concept of uncertainty as it applies to planning for resource-based regions through a scoping analysis of regional stakeholders' perceptions of land-use uncertainty. It then investigates solutions to alleviate such an issue.

The impact of litigation risk on the strategic timing of management earnings forecasts

This paper examines whether managers strategically time their earnings forecasts (MEFs) as litigation risk increases. We find as litigation risk increases, the propensity to release a delayed forecast until after the market is closed (AMC) or a Friday decreases but not proportionally more for bad news than for good news. Host costly this behaviour is to investors is questionable as share price returns do not reveal any under-reaction to strategically timed bad news MEF released AMC. We also find evidence consistent with managers timing their MEFs during a natural no-trading period to better disseminate information.

Uncertainty quantification applied to the performance analysis of a conical diffuser

This paper offers an uncertainty quantification (UQ) study applied to the performance analysis of the ERCOFTAC conical diffuser. A deterministic CFD solver is coupled with a non-statistical generalised Polynomial Chaos(gPC)representation based on a pseudo-spectral projection method. Such approach has the advantage to not require any modification of the CFD code for the propagation of random disturbances in the aerodynamic field. The stochactic results highlihgt the importance of the inlet velocity uncertainties on the pressure recovery both alone and when coupled with a second uncertain variable. From a theoretical point of view, we investigate the possibility to build our gPC representation on arbitray grid, thus increasing the flexibility of the stochastic framework.

Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation

Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2–6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values; however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled way.