Testing for homogeneity in demand systems when the regressors are nonstationary

An implication of optimizing theory is that demand functions are homogeneous of degree zero in prices and nominal income. Evidence based on estimations of demand systems has repeatedly found this restriction to be rejected by the data. However, the hypothesis is often formulated in terms of regressors that are non-stationary. This paper re-examines the evidence for homogeneity in light of recent developments in time-series econometrics with special emphasis on the treatment of trends. We find the demand system to be stochastically but not deterministically cointegrated. Using techniques developed for estimating cointegrating vectors in the presence of deterministic trends, we re-estimate the demand system and find that homogeneity holds in many cases.     

Estimating demand with distance functions: Parameterization in the primal and dual

Our purpose is to investigate the ability of different parametric forms to ‘correctly’ estimate consumer demands based on distance functions using Monte Carlo methods. Our approach combines economic theory, econometrics and quadratic approximation. We begin by deriving parameterizations for transformed quadratic functions which are linear in parameters and characterized by either homogeneity or which satisfy the translation property. Homogeneity is typical of Shephard distance functions and expenditure functions, whereas translation is characteristic of benefit/shortage or directional distance functions. The functional forms which satisfy these conditions and include both first- and second-order terms are the translog and quadratic forms, respectively. We then derive a primal characterization which is homogeneous and parameterized as translog and a dual model which satisfies the translation property and is specified as quadratic. We assess functional form performance by focusing on empirical violations of the regularity conditions. Our analysis corroborates results from earlier Monte Carlo studies on the production side suggesting that the quadratic form more closely approximates the ‘true’ technology or in our context consumer preferences than the translog.     

A neural network demand system with heteroskedastic errors

In this paper we consider estimation of demand systems with flexible functional forms, allowing an error term with a general conditional heteroskedasticity function that depends on observed covariates, such as demographic variables. We propose a general model that can be estimated either by quasi-maximum likelihood (in the case of exogenous regressors) or generalized method of moments (GMM) if the covariates are endogenous. The specification proposed in the paper nests several demand functions in the literature and the results can be applied to the recently proposed Exact Affine Stone Index (EASI) demand system of [Lewbel, A., Pendakur, K., 2008. Tricks with Hicks: The EASI implicit Marshallian demand system for unobserved heterogeneity and flexible Engel curves. American Economic Review (in press)]. Furthermore, flexible nonlinear expenditure elasticities can be estimated.     

Estimating high-dimensional demand systems in the presence of many binding non-negativity constraints

Two econometric issues arise in the structural estimation of consumer or producer demand systems in the presence of many binding non-negativity constraints. Firstly, most existing methods entail the evaluation of multivariate probability integrals. Secondly, the issue of statistical coherency must be addressed. We circumvent both of these issues using Gibbs’ Sampling, along with data augmentation and rejection sampling. We illustrate our method using several simulated data sets.     

Estimating the structural credit risk model when equity prices are contaminated by trading noises

The transformed-data maximum likelihood estimation (MLE) method for structural credit risk models developed by Duan [Duan, J.-C., 1994. Maximum likelihood estimation using price data of the derivative contract. Mathematical Finance 4, 155–167] is extended to account for the fact that observed equity prices may have been contaminated by trading noises. With the presence of trading noises, the likelihood function based on the observed equity prices can only be evaluated via some nonlinear filtering scheme. We devise a particle filtering algorithm that is practical for conducting the MLE estimation of the structural credit risk model of Merton [Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29, 449–470]. We implement the method on the Dow Jones 30 firms and on 100 randomly selected firms, and find that ignoring trading noises can lead to significantly over-estimating the firm’s asset volatility. The estimated magnitude of trading noise is in line with the direction that a firm’s liquidity will predict based on three common liquidity proxies. A simulation study is then conducted to ascertain the performance of the estimation method.     

Estimating the relative efficiency of various forms of prevention at different stages of a drug epidemic

Drug use and problems change dramatically over time in ways that are often described as reflecting an “epidemic cycle”. We use simulation of a model of drug epidemics to investigate how the relative effectiveness of different types of prevention varies over the course of such an epidemic. Specifically we use the so-called LHY model (see, Discussion Paper No. 251 of the Institute of Econometrics, OR, and Systems Theory, Vienna University of Technology, Vienna, Austria, 2000) which includes both “contagious” spread of initiation (a positive feedback) and memory of past use (a negative feedback), which dampens initiation and, hence, future use. The analysis confirms the common sense intuition that prevention is more highly leveraged early in an epidemic, although the extent to which this is true in this model is striking, particularly for campaigns designed to preserve or amplify awareness of the drug's dangers. The findings also suggest that the design of “secondary” prevention programs should change over the course of an epidemic.     

Information systems for collaborating versus transacting: Impact on manufacturing plant performance in the presence of demand volatility

Research at the nexus of operations management and information systems suggests that manufacturing plants may benefit from the utilization of information systems for collaborating and transacting with suppliers and customers. The objective of this study is to examine the extent to which value generated by information systems for collaborating versus transacting is contingent upon demand volatility. We analyze a unique dataset assembled from non-public U.S. Census Bureau data of manufacturing plants. Our findings suggest that when faced with volatile demand, plants employing information systems for collaborating with suppliers and customers experience positive and significant benefits to performance, in terms of both labor productivity and inventory turnover. In contrast, results suggest that plants employing information systems for transacting in volatile environments do not experience such benefits. Further exploratory analysis suggests that in the context of demand volatility, these two distinct dimensions of IT-based integration have differing performance implications at different stages of the production process in terms of raw-materials inventory and finished-goods inventory, but not in terms of work-in-process inventory. Taken together, our study contributes to theoretical and managerial understanding of the contingent value of information systems in volatile demand conditions in the supply chain context.     

Drivers and outcomes of open-standard interorganizational information systems assimilation in high-technology supply chains

In recent years, firms in high-technology supply chains have established internet-based electronic linkages with their trading partners. As a result, they have improved their ability to coordinate and synchronize shared business processes by using more complete, accurate, and timely information. These electronic linkages are based on open-standard interorganizational information systems (OSIOS), which are fundamentally different from traditional electronic data interchanges. OSIOS capture not only the technical specifications for data interchange but also the sequential steps for the execution of shared business processes. Because OSIOS are still at an early diffusion stage, it remains unclear why firms would assimilate such an innovation and whether assimilation provides firms any benefits. In this research, we develop a framework grounded on the economics of standards, institutional theory, and strategic interorganizational information systems literatures to investigate the drivers and outcomes of OSIOS assimilation in a focused context. In order to test our hypotheses based on this framework, we used data from a high-technology supply chain and employed econometrics techniques. We found that both competition asymmetry across supply chain echelons and OSIOS assimilation within supply chain echelons predict individual firms’ OSIOS assimilation. The results also suggest that firms’ supply chain dominance is both a driver and an outcome of OSIOS assimilation, highlighting a mutually reinforcing process. In addition, our study reveals boundary conditions of the hypothesized relationships. The use of multiple theoretical perspectives, a unique dataset, and innovative statistical techniques to investigate OSIOS assimilation in high-technology supply chains contributes to the body of knowledge in both the supply chain management and management of information systems disciplines.     

Concept-based query language approach to enterprise information systems

In enterprise information systems (EISs) it is necessary to model, integrate and compute very diverse data. In advanced EISs the stored data often are based both on structured (e.g. relational) and semi-structured (e.g. XML) data models. In addition, the ad hoc information needs of end-users may require the manipulation of data-oriented (structural), behavioural and deductive aspects of data. Contemporary languages capable of treating this kind of diversity suit only persons with good programming skills. In this paper we present a concept-oriented query language approach to manipulate this diversity so that the programming skill requirements are considerably reduced. In our query language, the features which need technical knowledge are hidden in application-specific concepts and structures. Therefore, users need not be aware of the underlying technology. Application-specific concepts and structures are represented by the modelling primitives of the extended RDOOM (relational deductive object-oriented modelling) which contains primitives for all crucial real world relationships (is-a relationship, part-of relationship, association), XML documents and views. Our query language also supports intensional and extensional–intensional queries, in addition to conventional extensional queries. In its query formulation, the end-user combines available application-specific concepts and structures through shared variables.     

High-performance work systems and organizational performance: testing the mediation role of employee outcomes using evidence from PR China

This research investigates the relationship between high-performance work systems (HPWS) and organizational performance, and the mediating effect of employee outcomes. The paper is based on a sample of 168 firms of six ownership types and in various business sectors operating in China. The results of data analysis support the hypotheses. Organizational performance is positively predicted by HPWS and employee outcomes, and employee outcomes positively mediate the relationship between HPWS and organizational performance. This paper supports the theory that HPWS positively impacts organizational performance and explains the mechanisms through which HPWS enhances organizational performance. It also responds to the long-standing call for stressing the importance of employee-related factors in the HRM–performance linkage.     

Big data for cyber physical systems in industry 4.0: a survey

ABSTRACT

With the technology development in cyber physical systems and big data, there are huge potential to apply them to achieve personalization and improve resource efficiency in Industry 4.0. As Industry 4.0 is the relatively new concept originated from an advanced manufacturing vision supported by the German government in 2011, there are only several existing surveys on either cyber physical systems or big data in Industry 4.0. In addition, there are much less surveys related to the intersection between cyber physical systems and big data in Industry 4.0. However, cyber physical systems are closely related to big data in nature. For example, cyber physical systems will continuously generate a large amount of data which requires the big data techniques to process and help to improve system scalability, security, and efficiency. Therefore, we conduct this survey to bring more attention to this critical intersection and highlight the future research direction to achieve the fully autonomy in Industry 4.0.     

THE EXTENDED ECONOMETRIC INPUT–OUTPUT MODEL WITH HETEROGENEOUS HOUSEHOLD DEMAND SYSTEM

This paper proposes an extension to the regional econometric input–output model (REIM) [Conway, R.S. (1990) The Washington Projection and Simulation Model: A Regional Interindustry Econometric Model. International Regional Science Review, 13, 141–165; Israilevich, P.R., G.J.D. Hewings, M. Sonis and G.R. Schindler (1997) Forecasting Structural Change with a Regional Econometric Input–Output Model. Journal of Regional Science, 37, 565–590]. We integrate a demand system with age and income parameters into the REIM. The extended model thus addresses concerns about the effects of household heterogeneity. The initial testing is conducted with a model for the Chicago metropolitan area. First, using aggregate expenditure data by income and age groups, the almost ideal demand system with group fixed effects is constructed. Next, the estimated demand system is linked to the REIM to reflect long-term changes in the age and income distribution of households. The long-range simulation from the extended model takes into account structural changes in expenditure type stemming from changing demographic composition. The extended model further broadens the scope of impact analysis under various scenarios associated with age and income changes.     

Linear rank tests for comparing treatments with a control when data are subject to unequal patterns of censorship

A class of asymptotically distribution-free tests is considered for comparing several treatments with a control when the, data are subject to unequal right-censorship. A particular member of this class is proposed for use in practice and an illustrative numerical example is given. A general result for the Pitman efficacy of a test based on an asymptotically normal test statistic is proved for the multiparameter case and using this result the efficacy of the proposed class of tests is obtained under sequences of translation and proportional hazards alternatives. Simulation studies are conducted to compare the performance of the proposed test, in terms of power, with some other tests.     

Healthcare information systems: data mining methods in the creation of a clinical recommender system

Recommender systems have been extensively studied to present items, such as movies, music and books that are likely of interest to the user. Researchers have indicated that integrated medical information systems are becoming an essential part of the modern healthcare systems. Such systems have evolved to an integrated enterprise-wide system. In particular, such systems are considered as a type of enterprise information systems or ERP system addressing healthcare industry sector needs. As part of efforts, nursing care plan recommender systems can provide clinical decision support, nursing education, clinical quality control, and serve as a complement to existing practice guidelines. We propose to use correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans. In the current study, we used nursing diagnosis data to develop the methodology. Our system utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items. Unlike common commercial systems, our system makes sequential recommendations based on user interaction, modifying a ranked list of suggested items at each step in care plan construction. We rank items based on traditional association-rule measures such as support and confidence, as well as a novel measure that anticipates which selections might improve the quality of future rankings. Since the multi-step nature of our recommendations presents problems for traditional evaluation measures, we also present a new evaluation method based on average ranking position and use it to test the effectiveness of different recommendation strategies.     

Identification of approximately duplicate material records in ERP systems

The quality of master data is crucial for the accurate functioning of the various modules of an enterprise resource planning (ERP) system. This study addresses specific data problems arising from the generation of approximately duplicate material records in ERP databases. Such problems are mainly due to the firm’s lack of unique and global identifiers for the material records, and to the arbitrary assignment of alternative names for the same material by various users. Traditional duplicate detection methods are ineffective in identifying such approximately duplicate material records because these methods typically rely on string comparisons of each field. To address this problem, a machine learning-based framework is developed to recognise semantic similarity between strings and to further identify and reunify approximately duplicate material records – a process referred to as de-duplication in this article. First, the keywords of the material records are extracted to form vectors of discriminating words. Second, a machine learning method using a probabilistic neural network is applied to determine the semantic similarity between these material records. The approach was evaluated using data from a real case study. The test results indicate that the proposed method outperforms traditional algorithms in identifying approximately duplicate material records.