Portfolio selection in a data-rich environment

We model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables. This approach (hereafter diffusion index approach) offers the opportunity to exploit a much richer information base to improve portfolio selection. We use factor analysis to estimate the space spanned by the factors. This provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity. We consider an empirical application to illustrate the practical usefulness of our approach. The results indicate that the diffusion index approach helps to improve the portfolio performance.     

Personnel development for dissolving jobs: towards a competency-based approach?

The traditional HRM system has been job-based and has reflected a bureaucratic model of organizing work. Such job-based personnel management systems have shown a tendency to break down when jobs are dissolving under more flexible work organization. Many writers have called for a competency approach to replace the traditional job-based approach under these new conditions, and we have seen a growing literature on competency modelling. The study reported here suggests that jobs are structured along two independent lines: formal control and complexity of work tasks. Each combination of formalization and complexity (high and low) gives rise to a different approach to align work and competencies. The job-based approach and the competency approach are only two of these, and an occupational and a professional approach are also identified. While formal company-internal training is related to job approach and competency approach, training for skilled tradesmen and professionals is a combination of company-external professional training prior to the work career and informal on-the-job and self-managed training.     

Developing a multivariate approach to monitor fuzzy quality profiles

Referring to several applications in which the response quality characteristic is fuzzy, this paper studies how the profile functional relationship between a fuzzy response variable and a predictor variable can be monitored by using a fuzzy regression model which is referred to as profile. The purpose of this paper is to develop a multivariate approach for monitoring process/product fuzzy quality profiles in phase I for applications where the quality characteristic is linguistic, imprecise, vague or deficient. The multivariate approach includes three fuzzy multivariate control charts which are developed by using fuzzy set theory to monitor fuzzy profiles in order to achieve an in-control process. The performance of developed approach is investigated on the basis of signal probability in various out-of-control scenarios through a simulation study. Compared with univariate approach, the results indicate a good performance of our multivariate approach in detecting all sized shifts in process profiles. A real case in tourism industry is utilized to show the applicability of the proposed approach.     

On becoming a sociomaterial researcher: Exploring epistemological practices grounded in a relational,performative ontology

What is the role of the researcher in a world that is continuously enacted and reconfigured in sociomaterial practices, a world in which subject and object, structure and agency, body and mind, knower and known, are assumed to be ontologically inseparable? In this article, I explore this question by drawing on my own experiences of reconsidering essentialist and representationalist assumptions, and becoming a sociomaterial researcher. My exploration draws on my experiences of conducting a qualitative longitudinal case study at the Swedish Migration Board. Specifically, I show what it can mean to ‘invite materiality’ into interviews, examine the conditions of possibility to become in certain ways by tracing the genealogy of practices, and engage with data relationally rather than categorically. By accounting for my experience of working through these practices, I aim to develop and articulate an understanding of what the ontological position underlying a sociomaterial approach implies for epistemology, and of how we can act (or, rather, intra-act) more creatively and responsibly as sociomaterial researchers. Moreover, I highlight differences in the kinds of knowledge that a sociomaterial approach grounded in relational and performative onto-epistemologies, as opposed to a socio-material approach, grounded in critical realism, produce about the unfolding of organizational practices—specifically, the practices unfolding in the reception area of the Swedish Migration Board. The paper contributes to the current debate on sociomaterial approaches, and in particular to the development of practices available to draw upon for researchers taking a sociomaterial approach.     

Using simulation as a tool in selecting a retirement age under defined benefit pension plans

This paper examines how simulation modeling can be used to select a retirement age under defined benefit pension plans. This approach construes the variables affecting pension benefits as probabilistic variables. Simulations are then run to generate probabilistic values for the real value of pension benefits for alternative retirement ages. By construing variables affecting pension benefits as probability distributions, this approach reflects the uncertainty facing individuals contemplating retirement. By generating estimates of retirement benefits as probability distributions rather than as single deterministic values, the model provides individuals with a more realistic and complete frame of reference for making the retirement decision. The author is grateful to an anonymous referee and Joachim Zietz. JEF editor, for helpful comments.     

Evaluating a sequential tree-based procedure for multivariate imputation of complex missing data structures

Item nonresponse in survey data can pose significant problems for social scientists carrying out statistical modeling using a large number of explanatory variables. A number of imputation methods exist but many only deal with univariate imputation, or relatively simple cases of multivariate imputation, often assuming a monotone pattern of missingness. In this paper we evaluate a tree-based approach for multivariate imputation using real data from the 1970 British Cohort Study, known for its complex pattern of nonresponse. The performance of this tree-based approach is compared to mode imputation and a sequential regression based approach within a simulation study.     

Nowcasting China’s GDP Using a Bayesian Approach

Real time nowcasting is an assessment of current-quarter GDP from timely released economic and financial series before the GDP figure is disseminated. Providing a reliable current quarter nowcast in real time based on the most recently released economic and financial monthly data is crucial for central banks to make policy decisions and longer-term forecasting exercises. In this study, we use dynamic factor models to bridge monthly information with quarterly GDP and achieve reduction in the dimensionality of the monthly data. We develop a Bayesian approach to provide a way to deal with the unbalanced features of the dataset and to estimate latent common factors. We demonstrate the validity of our approach through simulation studies, and explore the applicability of our approach through an empirical study in nowcasting the China’s GDP using 117 monthly data series of several categories in the Chinese market. The simulation studies and empirical study indicate that our Bayesian approach may be a viable option for nowcasting the China’s GDP.     

Optimization of the facility location-allocation problem in a customer-driven supply chain

This paper develops and applies an integrated multiple criteria decision making approach to optimize the facility location-allocation problem in the contemporary customer-driven supply chain. Unlike the traditional optimization techniques, the proposed approach, combining the analytic hierarchy process (AHP) and the goal programming (GP) model, considers both quantitative and qualitative factors, and also aims at maximizing the benefits of deliverer and customers. In the integrated approach, the AHP is used first to determine the relative importance weightings or priorities of alternative locations with respect to both deliverer oriented and customer oriented criteria. Then, the GP model, incorporating the constraints of system, resource, and AHP priority is formulated to select the best locations for setting up the warehouses without exceeding the limited available resources. In this paper, a real case study is used to demonstrate how the integrated approach can be applied to deal with the facility location-allocation problem, and it is proved that the integrated approach outperforms the traditional cost-based approach.     

Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods

The problem in estimating a social accounting matrix (SAM) for a recent year is to find an efficient and cost-effective way to incorporate and reconcile information from a variety of sources, including data from prior years. Based on information theory, the paper presents a flexible 'cross entropy' (CE) approach to estimating a consistent SAM starting from inconsistent data estimated with error, a common experience in many countries. The method represents an efficient information processing rule-using only and all information available. It allows incorporating errors in variables, inequality constraints, and prior knowledge about any part of the SAM. An example is presented, applying the CE approach to data from Mozambique, using a Monte Carlo approach to compare the CE approach to the standard RAS method and to evaluate the gains in precision from utilizing additional information.     

Daily peak electrical load forecasting with a multi-resolution approach

In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders in the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are (a) a general and formal introduction to the multi-resolution modelling approach, (b) a discussion of modelling approaches at different resolutions implemented via generalised additive models and neural networks, and (c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.     

List augmentation with model based multiple imputation: a case study using a mixed-outcome factor model

This study concerns list augmentation in direct marketing. List augmentation is a special case of missing data imputation. We review previous work on the mixed outcome factor model and apply it for the purpose of list augmentation. The model deals with both discrete and continuous variables and allows us to augment the data for all subjects in a company's transaction database with soft data collected in a survey among a sample of those subjects. We propose a bootstrap-based imputation approach, which is appealing to use in combination with the factor model, since it allows one to include estimation uncertainty in the imputation procedure in a simple, yet adequate manner. We provide an empirical case study of the performance of the approach to a transaction data base of a bank.     

Tracing VARDI coefficients: a proposal

ABSTRACT

We propose a new approach for tracing the so-called ‘value-added-(re)distribution-important coefficients’ (in short the VARDI coefficients) in a world input–output model. From the perspective of a selected group of economies, VARDI coefficients may be defined as those elements in world input–output matrix in the case of which a small change in their levels leads to the maximization of a share of this group of economies in value added in global value chains. Due to the rapid development of the World Input Output Database, this approach may be easily applied in empirical research to different groups of countries and sectors in world IO models. In an illustrative empirical case study, we use the new approach in order to answer a question regarding what the main directions of the future macroeconomic policy of the U.S. could be in order to ensure the maximization of the country’s share in global value added.     

Bayesian MCMC Mapping of Quantitative Trait Loci in a Half-sib Design: a Graphical Model Perspective

Graphical models provide a powerful and flexible approach to the analysis of complex problems in genetics. While task-specific software may be extremely efficient for any particular analysis, it is often difficult to adapt to new computational challenges. By viewing these genetic applications in a more general framework, many problems can be handled by essentially the same software. This is advantageous in an area where fast methodological development is essential. Once a method has been fully developed and tested, problem-specific software may then be required. The aim of this paper is to illustrate the potential use of a graphical model approach to genetic analyses by taking a very simple and well-understood problem by way of example.     

Factor vector autoregressive estimation: a new approach

In this paper a new approach to factor vector autoregressive estimation, based on Stock and Watson (Implications of dynamic factor models for VAR analysis, NBER Working Paper, no. 11467, 2005), is introduced. In addition to sharing all the relevant features of the Stock–Watson approach, in its static formulation, the proposed method has the advantage of allowing for a more clear-cut interpretation of the global factors, as well as for the identification of all idiosyncratic shocks. An application to large-scale macroeconometric modelling is also provided. The authors are grateful to an anonymous referee for constructive comments and to MIUR (PRIN project 2005) for financial support.     

Developing a sustainability ethic in leaders

The triple bottom line of fiscal, social, and environmental success considerably alters how organizations (and stakeholders) measure sustainable success. More important, however, is the conceptual shift required to understand and successfully lead organizations within this increasingly accepted paradigm. This article uses a mixed‐methods approach to explore the conceptual development of a sustainability ethic in aspiring leaders. More specifically, the authors report research examining the curriculum and pedagogy of a leadership education effort, in this case a college‐level course, aimed at effecting change in both individual aspiring leaders and their immediate community. The article describes the philosophical and research base for the curriculum and specific activities with their pedagogical approach. Results show evidence of increased awareness, importance, and commitment to sustainability as well as changes in how students conceptualize sustainability leadership. Findings offer insights for further developing leadership education for sustainability.     

CLOE: a cross-lingual ontology enrichment using multi-agent architecture

ABSTRACT

This article proposes across-lingual ontology enrichment (CLOE) approach for enriching ontologies from amultilingual text or other ontologies. Athree-level hierarchical multi-agent model has been built, where each node is modelled as an agent. This has several advantages: ontologies can be used to enrich other ontologies in adifferent language, several ontologies can be enriched at the same time, and domain-independent. A prototype for the proposed approach has been implemented. Evaluation results are promising, showing considerable precision and recall scores. We believe that this approach is the first such attempt to enrich multiple ontologies using adomain-independent approach.     

Calibrating longitudinal models of residential mobility and migration: an assessment of a non-parametric marginal likelihood approach

"This paper discusses the problems of controlling for omitted variables in estimating the structural parameters of longitudinal models and focuses upon an assessment of a non-parametric marginal maximum likelihood approach suggested by the results of Laird....The approach is shown to be statistically valid for a plausible discrete-time model of the incidence of residential or migration moves, at least for data in which no household moves in every time period. Empirical evaluation with two large [U.S.] datasets on residential mobility indicates that the approach is also computationally feasible and provides a promising alternative to more conventional methods for controlling for omitted variables."     

Semiparametric estimation of a truncated regression model

This paper proposes a new semiparametric estimator for the truncated regression model under the independence restriction. Many existing approaches such as those in Lee (1992) and Honoré and Powell (1994) are moment-based methods, whereas our approach makes use of the entire truncated distribution. As a result, our approach is expected to require weaker identification and to have more favorable performance. Our simulation results suggest that our estimator outperforms that of Lee (1992) and Honoré and Powell (1994) in a variety of designs. Our estimator is shown to be consistent and asymptotically normal.     

Hierarchical forecasting with a top-down alignment of independent-level forecasts

Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Extensive research focuses on improving the accuracy of each hierarchy, especially the intermittent time series at bottom levels. Then, hierarchical reconciliation can be used to improve the overall performance further. In this paper, we present a hierarchical-forecasting-with-alignment approach that treats the bottom-level forecasts as mutable to ensure higher forecasting accuracy on the upper levels of the hierarchy. We employ a pure deep learning forecasting approach, N-BEATS, for continuous time series at the top levels, and a widely used tree-based algorithm, LightGBM, for intermittent time series at the bottom level. The hierarchical-forecasting-with-alignment approach is a simple yet effective variant of the bottom-up method, accounting for biases that are difficult to observe at the bottom level. It allows suboptimal forecasts at the lower level to retain a higher overall performance. The approach in this empirical study was developed by the first author during the M5 Accuracy competition, ranking second place. The method is also business orientated and can be used to facilitate strategic business planning.     

Inference and prediction in a multiple-structural-break model

This paper develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. The model has several desirable features. First, the number of regimes is not fixed but is treated as a random variable. Second, the model adopts a hierarchical prior for regime coefficients, which allows for the coefficients of one regime to contain information about coefficients of other regimes. Third, the regime coefficients can be integrated analytically in the posterior density; as a consequence the posterior simulator is fast and reliable. An application to US real GDP quarterly growth rates links groups of regimes to specific historical periods and provides forecasts of future growth rates.