Specific Data Model Research Articles

Bayesian semiparametric hierarchical empirical likelihood spatial models

We introduce a general hierarchical Bayesian framework that incorporates a flexible nonparametric data model specification through the use of empirical likelihood methodology, which we term semiparametric hierarchical empirical likelihood (SHEL) models. Although general dependence structures can be readily accommodated, we focus on spatial modeling, a relatively underdeveloped area in the empirical likelihood literature. Importantly, the models we develop naturally accommodate spatial association on irregular lattices and irregularly spaced point-referenced data. We illustrate our proposed framework by means of a simulation study and through three real data examples. First, we develop a spatial Fay–Herriot model in the SHEL framework and apply it to the problem of small area estimation in the American Community Survey. Next, we illustrate the SHEL model in the context of areal data (on an irregular lattice) through the North Carolina sudden infant death syndrome (SIDS) dataset. Finally, we analyze a point-referenced dataset from the North American Breeding Bird Survey that considers dove counts for the state of Missouri. In all cases, we demonstrate superior performance of our model, in terms of mean squared prediction error, over standard parametric analyses.

A metadata model and mapping approach for facilitating access to heterogeneous cultural heritage assets

In the last decade, Europe has put a tremendous effort into making cultural, educational and scientific resources publicly available. Based on national or thematic aggregators, initiatives like Europeana nowadays provide a plethora of cultural resources for people worldwide. Although such massive amounts of rich cultural heritage content are available, the potential of its use for educational and scientific purposes still remains largely untapped. Much valuable content is only available in the so-called long tail, i.e. in niche resources such as specifically themed cultural heritage collections, and are difficult to access from the mainstream hubs like major search engines, social networks or online encyclopaedias. The vision of the EEXCESS project is to push high-quality content from the long tail to platforms and devices which are used every day. The realisation of such use cases requires as a basis (and in addition to the functional components) a common metadata representation and tools for mapping between the data sources' specific data models and this common representation. In this paper, we propose a data model for such a system that combines federated search results from different cultural heritage data sources. We then propose an approach for metadata mapping, with a focus on easy configurability of mappings, which--once properly configured--can then be executed on the fly by an automatic service. We demonstrate the approach using a real-world example.

Business Collaboration in Food Networks: Incremental Solution Development

The paper will present an approach for an incremental solution development that is based on the usage of the currently developed Internet based FIspace business collaboration platform. Key element is the clear segmentation of infrastructures that are either internal or external to the collaborating business entity in the food network. On the one hand, the approach enables to differentiate between specific centralised as well as decentralised ways for data storage and hosting of IT based functionalities. The selection of specific data exchange protocols and data models is facilitated. On the other hand, the supported solution design and subsequent development is focusing on reusable “software Apps” that can be used on their own and are incorporating a clear added value for the business actors. It will be outlined on how to push the development and introduction of Apps that do not require basic changes of the existing infrastructure. The paper will present an example that is based on the development of a set of Apps for the exchange of product quality related information in food networks, specifically addressing fresh fruits and vegetables. It combines workflow support for data exchange from farm to retail as well as to provide quality feedback information to facilitate the business process improvement. Finally, the latest status of the FIspace platform development will be outlined. Key features and potential ways for “real users and software developers” in using the FIspace platform that is initiated by science and industry will be outlined.

Spatial econometric panel data model specification: A Bayesian approach

Taking a Bayesian perspective on model uncertainty for static panel data models proposed in the spatial econometrics literature considerably simplifies the task of selecting an appropriate model. A wide variety of alternative specifications that include various combinations of spatial dependence in lagged values of the dependent variable, spatial lags of the explanatory variables, as well as dependence in the model disturbances have been the focus of a literature on various statistical tests for distinguishing between these numerous specifications.A Bayesian model uncertainty argument is advanced that logically implies we can simplify this task by focusing on only two model specifications. One of these, labeled the spatial Durbin model (SDM) implies global spatial spillovers, while the second, labeled a spatial Durbin error model (SDEM) leads to local spatial spillovers. A Bayesian approach to determining an appropriate local or global specification, SDEM versus SDM is set forth here for static panel variants of these two models. The logic of the Bayesian view of model uncertainty suggests these are the only two specifications that need to be considered. This greatly simplifies the task confronting practitioners when using static panel data models.

Code interoperability and standard data formats in quantum chemistry and quantum dynamics: The Q5/D5Cost data model

Code interoperability and the search for domain-specific standard data formats represent critical issues in many areas of computational science. The advent of novel computing infrastructures such as computational grids and clouds make these issues even more urgent. The design and implementation of a common data format for quantum chemistry (QC) and quantum dynamics (QD) computer programs is discussed with reference to the research performed in the course of two Collaboration in Science and Technology Actions. The specific data models adopted, Q5Cost and D5Cost, are shown to work for a number of interoperating codes, regardless of the type and amount of information (small or large datasets) to be exchanged. The codes are either interfaced directly, or transfer data by means of wrappers; both types of data exchange are supported by the Q5/D5Cost library. Further, the exchange of data between QC and QD codes is addressed. As a proof of concept, the H + H2 reaction is discussed. The proposed scheme is shown to provide an excellent basis for cooperative code development, even across domain boundaries. Moreover, the scheme presented is found to be useful also as a production tool in the grid distributed computing environment.

Studying immigrant earnings with alternative data sources and model specifications: A reply to DeVoretz’s comment

Author's response.

A Windchill-Based PDM Application to Enhance Agricultural Machinery Chassis Design

Considering the actual requirements of data and process management in agricultural machinery product design, a Windchill-based PDM system is proposed for agricultural chassis products design regarding its system architecture, data models and functional modules. Specific workflow models and data models are built to provide a collaborative platform for the product data management. It consists of seven major functional modules such as data management, data search, management of team, version and permission and system integration, etc. Its implementation methods will provide a useful guide and application for the agricultural machinery manufacturing enterprises to achieve rapid design of their products.

Decentralized Distribution Update Algorithm Based on Compressibility-controlled Wireless Sensor Network

Sensor network adopts the lossy compression techniques to collect long-term data, analyze the data tendency and the interested specific data model. In these applications, the sensor is established to collect large numbers of continuous data, and allow the access to the lossy and untimely data. In addition, the neighbor sensor data is correlated both in time and in space. Therefore, the data sensed by the sensor itself in the intermediate node and the data will be lossy compressed for prolonging the system operation lifetime. To study the optimal distributed problem of the bite-rate and the lossy degree. When the optimal distribution problem between the bit-rate and the lossy degree is discussed, how to make the optimal decision distributes the compressibility of all sensors in the satisfaction of the acceptable data distorted condition. For adopting the minimum transmittal bit-rate to collect the top-quality data. The optimal solution is introduced for the distribution problem, and the decentralized distribution algorithm is introduced in terms of the optimal solution. Compared with the average distribution strategy, the simulation result shows that the optimal solution and the decentralized actually can reduce large numbers of the network transmittal data volume

CIM 기반의 배전자동화 시뮬레이터

The main purpose of the distribution automation system (DAS) is to achieve efficient operation of primary distribution systems by monitoring and control of the feeder remote terminal unit(FRTU) deployed on the distribution feeders. DAS simulators are introduced to verify the functions of the application software installed in the central control unit(CCU) of the DAS. Because each DAS is developed on the basis of its own specific data model, the power system data cannot be easily transferred from the DAS to the simulator or vice versa. This paper presents a common information model(CIM)-based DAS simulator to achieve interoperability between the simulator and the DASs developed by different vendors. The CIM-based data model conversion between Smart DMS (SDMS) and Total DAS (TDAS) has been performed to establish feasibility of the proposed scheme.

A Sequential Monte Carlo Approach for MLE in a Plant Growth Model

Parametric identification of plant growth models formalized as discrete dynamical systems is a challenging problem due to specific data acquisition (system observation is generally done with destructive measurements), non-linear dynamics, model uncertainties and high-dimensional parameter space. In this study, we present a novel idea of modeling plant growth in the framework of non-homogeneous hidden Markov models (Cappé, Moulines, and Rydén 2005), for a certain class of plants with known organogenesis (structural development). Unknown parameters of the models are estimated via a stochastic variant of a generalized EM (Expectation-Maximization) algorithm and approximate confidence intervals are given via parametric bootstrap. The complexity of the model makes both the E-step (expectation step) and the M-step (maximization step) non-explicit. For this reason, the E-step is approximated via a sequential Monte Carlo procedure (sequential importance sampling with resampling) and the M-step is separated into two steps (Conditional-Maximization), where before applying a numerical maximization procedure (quasi-Newton type), a large subset of unknown parameters is updated explicitly conditioned on the other subset. A simulation study and a case-study with real data from the sugar beet are considered and a model comparison is performed based on these data. Appendices are available online.

Spatial Econometric Panel Data Model Specification: A Bayesian Approach

Taking a Bayesian perspective on model uncertainty for static panel data models proposed in the spatial econometrics literature considerably simplifies the task of selecting an appropriate model. A wide variety of alternative specifications that include various combinations spatial dependence in lagged values of the dependent variable, spatial lags of the explanatory variables, as well as dependence in the model disturbances have been the focus of a literature on various statistical tests for distinguishing between these numerous specifications.

English

  We investigate the risk-return relation in the South African stock market using data covering the period from 1973 to 2011. Prior research for several countries reveals high sensitivity of the results to data details and models used. Therefore, our analysis of the risk-return nexus in South Africa are based on three different data frequencies (weekly, monthly and quarterly) and are derived from three different generalized autoregressive conditional heteroskedasticity (GARCH) models in addition to a plain vanilla time-series approach. Similar to the findings of Glosten et al. in 1993 and Harvey in 2001, our results fail to support a significantly positive risk-return relationship in South Africa across various data frequencies and model specifications, and this conclusion survives further robustness checks using different sub-periods and index data. Our results further suggest that the recent global financial crisis may have altered market dynamics and distorted the risk-return relation in the South African stock market.   Key words: Risk-return relationship, volatility models, conditional variance, South African stock market.

Have State Renewable Portfolio Standards Really Worked? Synthesizing Past Policy Assessments to Build an Integrated Econometric Analysis of RPS effectiveness in the U.S.

Renewable portfolio standards (RPS) are the most popular U.S. state-level policy tools for promoting deployment of electricity from renewable energy (RES-E). While several econometric studies have estimated the effect of RPS policies on RES-E deployment, results are sometimes contradictory. Thus, there is a need to not only reconcile these studies but also establish a definitive answer to the question whether RPS policies have been effective in increasing RES-E deployment. We use a panel – 50 states over 1990-2010 – of renewable deployment, policy design elements and market dynamics to capture the heterogeneity in state RPS design and state characteristics. We run time series cross sectional regressions with fixed effects, and verify the causal effect implied by our results with a nonparametric matching analysis. We trace discrepancies among past findings to differences in data classification and model specification. We show that the key feature of RPS policies – RPS stringency – has no effect, and in some cases a negative effect, on RES-E deployment. We also investigate the impact of individual RPS features. We show that renewable energy certificate (REC) unbundling as well as the presence of RPS in neighboring states is correlated with greater RES-E deployment locally, while other features – such as allowing greater flexibility to buy renewable energy from out-of-state – slows in-state RES-E deployment. We show that mandatory green power options have a positive effect on RES-E deployment while net metering schemes appear to have a negative impact. However, we were unable to detect any impact of an interaction effect between RPS and these policies. Finally, we investigate the impact of policies on the deployment of individual renewable technologies (biomass, geothermal, solar, and wind) and demonstrate differentiated policy impacts, such as biomass driving the result on RPS stringency.

Testing for persistent deviations of stock prices to dividends in the Nasdaq index

In this paper we test for the presence of bubbles in the Nasdaq stock market index over the period 1994–2003 applying fractional integration techniques and allowing for structural breaks and non-linear adjustments of prices to dividends. The results show a significant structural break in 1998 for all model specifications and data periodicity. Furthermore, we do not find evidence of asymmetric adjustment of prices to dividends when using M-TAR and TAR models. The evidence of bubbles varies depending on the data periodicity and model specification used in the analysis. Finally, the results show persistent deviations of stock prices to dividends in all cases considered, though we only find evidence of bubbles in the Nasdaq index when using weekly data for the time period after June 1998.

A simple indicator of systemic risk

We examine the relevance and effectiveness of stock return correlations among financial institutions as an indicator of systemic risk. By analyzing the trends and fluctuations of daily stock return correlations and default correlations among the 22 largest bank holding companies and investment banks from 1988 to 2008, we find that daily stock return correlation is a simple, robust, forward-looking, and timely systemic risk indicator. There is an increasing trend in stock return correlation among banks, whereas there is no obvious correlation trend among non-banks. We also disaggregate the stock returns into systematic and idiosyncratic components and find that the correlation increases are largely driven by the increases in correlations between banks’ idiosyncratic risks, which give rise to increasing systemic risk. Correlation spikes tend to predict or coincide with significant economic or market events, especially during the 2007–2008 financial crisis. Furthermore, we show that stock return correlations offer a perspective on the level of systemic risk in the financial sector that is not already captured by default correlations. Stock return correlations are not subject to data limitations or model specification errors that other potential systemic risk measures may face. Therefore, we recommend that regulators and businesses monitor daily stock return correlations among those large and highly leveraged financial institutions to track the level of systemic risk.

The Budget Deficit Debate: A Review And Further Analysis

This article presents a review of the results of some recent econometric studies, including our own, on the controversial issue regarding the budget deficit-interest rate relationship. The conclusions reached by the authors of these studies are mixed and sometimes contradictory. Differences in data bases, model specification, sample used, and variable definitions are some possible reasons for these varying conclusions.

Dynamic spatial panels: models, methods, and inferences

This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent variable lagged in space, a dependent variable lagged in both space and time, independent variables lagged in time, independent variables lagged in space, serial error autocorrelation, spatial error autocorrelation, spatial-specific and time-period-specific effects. The survey also examines the reasoning behind different model specifications and the purposes for which they can be used, which should be useful for practitioners.

A Simple Indicator of Systemic Risk

We examine the relevance and effectiveness of stock return correlations among financial institutions as an indicator of systemic risk. By analyzing the trends and fluctuations of daily stock return correlations and default correlations among the 22 largest bank holding companies and investment banks from 1988 to 2008, we find that daily stock return correlation is a simple, robust, forward-looking, and timely systemic risk indicator. There is an increasing trend in stock return correlation among banks, whereas there is no obvious correlation trend among non-banks. Correlation spikes tend to predict or coincide with significant economic or market events, especially during the 2007-2008 financial crisis. Furthermore, stock return correlations can offer a perspective on the level of systemic risk in the financial sector that is not already captured by default correlations. Stock return correlations are not subject to data limitations or model specification errors that other potential systemic risk measures may face. Therefore, we recommend that regulators and businesses monitor daily stock return correlations among financial institutions to track the level of systemic risk.

The empirical relevance of the competitive storage model

The empirical relevance of models of competitive storage arbitrage in explaining commodity price behavior has been seriously challenged in a series of pathbreaking papers by Deaton and Laroque (1992, 1995, 1996). Here we address their major criticism, that the model is in general unable to explain the degree of serial correlation observed in the prices of twelve major commodities. First, we present a simple numerical version of their model which, contrary to Deaton and Laroque (1992), can generate the high levels of serial correlation observed in commodity prices, if it is parameterized to generate realistic levels of price variation. Then, after estimating the Deaton and Laroque (1995, 1996) model using their data set, model specification and econometric approach, we show that the use of a much finer grid to approximate the equilibrium price function yields quite different estimates for most commodities. Results are obtained for coffee, copper, jute, maize, palm oil, sugar and tin that support the specifications of the storage model with positive constant marginal storage cost and no deterioration as in Gustafson (1958a). Consumption demand has a low response to price and, except for sugar, stockouts are infrequent. The observed magnitudes of serial correlation of price match those implied by the estimated model.

Some recent developments in spatial panel data models

Spatial econometrics has been an ongoing research field. Recently, it has been extended to panel data settings. Spatial panel data models can allow cross sectional dependence as well as state dependence, and can also enable researchers to control for unknown heterogeneity. This paper reports some recent developments in econometric specification and estimation of spatial panel data models. We develop a general framework and specialize it to investigate different spatial and time dynamics. Monte Carlo studies are provided to investigate finite sample properties of estimates and possible consequences of misspecifications. Two applications illustrate the relevance of spatial panel data models for empirical studies.