Barriers to capital accumulation in a model of technology adoption and schooling

Standard growth models require large differences in barriers to capital accumulation to reproduce the observed disparities in the wealth of nations. I introduce technology adoption and schooling decisions into a standard growth model and show that the required differences in barriers implied by this model are much smaller. In particular, a calibrated version of the model implies per capita income differences 3 times larger than a standard model. Per capita income differences are amplified by two reinforcing factors: schooling capital differences and aggregate total factor productivity differences. The results suggest caution in the role of factor inputs derived from standard development accounting exercises. A development policy that subsidizes education is not optimal in the presence of barriers to capital accumulation, removing barriers can replicate educational outcomes and generate higher income levels by several orders of magnitude.     

Corporate environmental performance evaluation: a measurement model and a new concept

While many third‐party organizations are practically evaluating corporate environmental performance (CEP), few academic studies have paid attention to third‐party environmental performance evaluation (EPE). To answer the question of what should be measured for third‐party EPE, we develop an environmental performance measurement (EPM) model consisting of environmental management performance (EMP) and environmental operational performance (EOP), and hypothesize that EMP be measured by four management performance indicators (MPIs: organizational system, stakeholder relations, operational countermeasures and environmental tracking) and EOP be measured by two operational performance indicators (OPIs: inputs and outputs). Further, to answer the question of how to enable third‐party EPE comparable across companies from different (sub‐)sectors, we propose to use the environmental intensity change index (EICI) as a measure of OPIs. Empirical tests confirm that the EICI and the evaluation based on it are comparable across companies from different sub‐sectors. Empirical tests also support the existence of the MPIs and OPIs and the two‐dimensioned constructs of CEP. Copyright © 2006 John Wiley & Sons, Ltd and ERP Environment.     

仓储企业核心竞争力的评价方法研究

通过对企业核心竞争力的分析研究，结合仓储企业的行业特点．从物流业务能力、市场营销能力，组织协调能力和资本运营能力四个方面构建评价仓储企业核心竞争力的指标体系，并应用综合模糊评价模型对仓储企业核心竞争力进行评价。     

A systematic approach to prioritizing R&D projects based on customer-perceived value using opinion mining

As product development has recently emphasized user innovation, it should necessarily reflect customer-perceived value, as well as technological value itself. However, while previous studies for technology planning have focused on analyzing the potential of technology, they have not considered the customer-perceived value that technology can create in a new product. Therefore, this research suggests a new approach to assessing the level of technology and evaluating R&D projects, by investigating customer-perceived value on technology through the use of the structural equation model and opinion mining. For this, the assessment framework is developed in terms of technology, product quality, and customer satisfaction, respectively, by investigating a variety of databases on each factor. The relationship between technology level and customer satisfaction is analyzed, using structural equation model and opinion mining. Based on the results, a strategy for technology development is established through gap analysis and simulation, after selecting and evaluating technologies that need to be developed. The proposed approach is applied to the real case of a moving system, in particular an automobile door, and we obtained that an R&D project for hinge-related technology would be promising, enhancing the customer satisfaction. It can suggest a future direction for new technology development. This paper contributes to enhancing the efficiency of technology planning based on the customer-perceived value, enabling the launch of new R&D projects.     

Relating ultrasonic vocalizations from a pair of rats to individual behavior: A composite link model approach

Ultrasonic vocalizations (USVs) are crucial in the social behavior of rats. We aim to relate USV rates of pairs of rats to individual activity in an automated home cage (PhenoTyper®) where USVs are recorded per pair and not per individual. We propose a composite link model approach to parametrize a mechanistic “sum‐of‐rates” model in which the pair's USV rate is the sum of the USV rates of individuals depending on their own behavior. In generalized linear models (GLMs), the individual's USV rates are multiplied. We verified through simulation that composite link model gave lower Poisson deviance than GLM. We analyzed the data from an experiment in which half of the cages did allow the pairs to interact (Pair Housing) and the other half did not (Individual Housing). The “sum‐of‐rates” model fits best for Individual Housing and GLM for Pair Housing. An additional simulation study strongly suggests that interaction between rats changes the underlying mechanism for vocalization behavior.     

低碳校园建设与“奔驰模型”构建研究——基于华南理工大学的调研分析

高等院校进行低碳校园建设,需要学校实施低碳政策,对学校相关部门、后勤服务中心、个人以及学生组织四个方面产生综合影响。文章通过对华南理工大学的调研,了解低碳校园建设现状及存在问题,最后从学校的角度出发提出建议方案,构建了低碳校园建设的"奔驰模型"。     

A new marked point process model for the federal funds rate target: Methodology and forecast evaluation

Forecasts of key interest rates set by central banks are of paramount concern for investors and policy makers. Recently it has been shown that forecasts of the federal funds rate target, the most anticipated indicator of the Federal Reserve Bank's monetary policy stance, can be improved considerably when its evolution is modeled as a marked point process (MPP). This is due to the fact that target changes occur in discrete time with discrete increments, have an autoregressive nature and are usually in the same direction. We propose a model which is able to account for these dynamic features of the data. In particular, we combine Hamilton and Jordà's [2002. A model for the federal funds rate target. Journal of Political Economy 110(5), 1135–1167] autoregressive conditional hazard (ACH) and Russell and Engle's [2005. A discrete-state continuous-time model of financial transactions prices and times: the autoregressive conditional multinomial-autoregressive conditional duration model. Journal of Business and Economic Statistics 23(2), 166 – 180] autoregressive conditional multinomial (ACM) model. The paper also puts forth a methodology to evaluate probability function forecasts of MPP models. By improving goodness of fit and point forecasts of the target, the ACH–ACM qualifies as a sensible modeling framework. Furthermore, our results show that MPP models deliver useful probability function forecasts at short and medium term horizons.     

Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database

Technology opportunity analysis has been the subject of many prior studies, although most of them have focused on discovering new technology ideas in a single narrow domain. This study proposes a product landscape analysis to identify product areas (i.e., potential technology opportunities) across multiple domains that firms can enter based on the technological capabilities embodied in their existing products. First, text mining is used to construct an integrated patent-product database from the United States patent and trademark database. Second, word2vec is employed to construct a product landscape as a vector space model where products with similar technological bases are located close to each other while maintaining the technological relationships. Third, given a product of interest, potential technology opportunities are identified via (1) automatic opportunity analysis that identifies product areas with technological bases similar to those of the product; and (2) interactive opportunity analysis that finds product areas based on experts’ queries modifying the technological bases of the product (i.e., addition and subtraction). Finally, ten quantitative indexes are developed to explore the implications of the potential technology opportunities identified. The case study covering 3,016,315 patents and 160,832 products confirms that the proposed approach is valuable as a creativity support tool for technology opportunity analysis in the era of convergence.     

A new approach to estimating a profit frontier using the censored stochastic frontier model

The translog profit functional form is widely used to study technical efficiency for banks. Although this functional form is known as being flexible, it is not applicable to those banks incurring economic losses. The recently developed approach, i.e., the censored stochastic frontier model (CSFM), by Tsay et al. (2013) appears to be superior to existing approaches, since CSFM does not need to transform negative profit into positive profit before taking the natural logarithm. The transformation with respect to the profit variable tends to bias the parameter estimates of the profit frontier and the subsequent profit efficiency measure. We show that the parameter estimates of CSFM have the desirable statistical properties. Moreover, empirical results reveal that the mean profit efficiency of CSFM is more robust than those models using transformed profits across the sub-periods 1991–1998 and 1999–2009.     

Fuzzy item response model: a new approach to generate membership function to score psychological measurement

The aim of this study is to propose an new approach, fuzzy item response model (FIRM), which combines item response theory (IRT) and fuzzy set theory, in the educational or psychological measurement. Applying FIRM to improve the predictive validity of psychological measurement is verified. We set up a detailed procedure for the FIRM and apply it to a valuable empirical study with Beck Depression Inventory-II (Chinese version) administrated on outpatient diagnosed as depression was given. The results showed the correct classification of depression based on FIRM scoring was 80.3% while that of raw score was only 73.2%. That is, via FIRM scoring, 7.9% of the erroneous judgments of depression inferred from self-reported inventory were reduced. It is also suggested that considerable cost concerning prevention and cure of depression might be reduced via FIRM.     

A resilience-based model for performance evaluation of information systems: the case of a gas company

Information systems (IS) are strongly influenced by changes in new technology and should react swiftly in response to external conditions. Resilience engineering is a new method that can enable these systems to absorb changes. In this study, a new framework is presented for performance evaluation of IS that includes DeLone and McLean’s factors of success in addition to resilience. Hence, this study is an attempt to evaluate the impact of resilience on IS by the proposed model in Iranian Gas Engineering and Development Company via the data obtained from questionnaires and Fuzzy Data Envelopment Analysis (FDEA) approach. First, FDEA model with α-cut = 0.05 was identified as the most suitable model to this application by performing all Banker, Charnes and Cooper and Charnes, Cooper and Rhodes models of and FDEA and selecting the appropriate model based on maximum mean efficiency. Then, the factors were ranked based on the results of sensitivity analysis, which showed resilience had a significantly higher impact on the proposed model relative to other factors. The results of this study were then verified by conducting the related ANOVA test. This is the first study that examines the impact of resilience on IS by statistical and mathematical approaches.     

The theta model: a decomposition approach to forecasting

This paper presents a new univariate forecasting method. The method is based on the concept of modifying the local curvature of the time-series through a coefficient ‘Theta’ (the Greek letter θ), that is applied directly to the second differences of the data. The resulting series that are created maintain the mean and the slope of the original data but not their curvatures. These new time series are named Theta-lines. Their primary qualitative characteristic is the improvement of the approximation of the long-term behavior of the data or the augmentation of the short-term features, depending on the value of the Theta coefficient. The proposed method decomposes the original time series into two or more different Theta-lines. These are extrapolated separately and the subsequent forecasts are combined. The simple combination of two Theta-lines, the Theta=0 (straight line) and Theta=2 (double local curves) was adopted in order to produce forecasts for the 3003 series of the M3 competition. The method performed well, particularly for monthly series and for microeconomic data.     

Economic convergence: Policy implications from a heterogeneous agent model

In this paper we study the effectiveness of different types of cohesion policies with respect to convergence of regions. A two-region agent-based macroeconomic model is used to analyze short-, medium- and long-term effects of policies improving human capital and fostering adoption of technologies in lagging regions. With fully integrated labor markets the human capital policy positively affects the economically stronger region but reduces production in the targeted weaker region. Subsidies for high technology investment in the weaker region have a positive local output effect and a negative effect on the neighboring region, thereby fostering convergence. When labor markets are not integrated both policies support convergence.     

The RWDAR model: A novel state-space approach to forecasting

This paper introduces the Random Walk with Drift plus AutoRegressive model (RWDAR) for time-series forecasting. Owing to the presence of a random walk plus drift term, this model shares some similarities with the Theta model of Assimakopoulos and Nikolopoulos (2000). However, the addition of a first-order autoregressive term in the state equation provides additional adaptability and flexibility. Indeed, it is shown that RWDAR tends to outperform the Theta model when forecasting both stationary and nearly non-stationary time series. This paper also proposes a simple estimation method for the RWDAR model based on the solution of the algebraic Riccati equation for the prediction error covariance of the state vector. Simulation results show that this estimator performs as well as the standard Kalman filter approach. Finally, using yearly data from the M3 and M4 competition datasets, it is found that RWDAR outperforms traditional forecasting methods.     

Estimating the output gap in real time: A factor model approach

By using a dynamic factor model, we can substantially improve the reliability of real-time output gap estimates for the U.S. economy. First, we use a factor model to extract a series for the common component in GDP from a large panel of monthly real-time macroeconomic variables. This series is immune to revisions to the extent that revisions are due to unbiased measurement errors or idiosyncratic news. Second, our model is able to handle the unbalanced arrival of the data. This yields favorable nowcasting properties and thus starting conditions for the filtering of data into a trend and deviations from a trend. Combined with the method of augmenting data with forecasts prior to filtering, this greatly reduces the end-of-sample imprecision in the gap estimate. The increased precision has economic importance for real-time policy decisions and improves real-time inflation forecasts.     

The success of job applications: a new approach to program evaluation

This paper suggests a novel approach to program evaluation that allows identification of the causal effect of a training program on the likelihood of being invited to a job interview under weak assumptions, i.e., by measuring the program-effects by pre- and post-treatment data that are very close in time for the same individual.     

检视都市永续发展指针的发展历程与内涵:从指针系统建构到政策评估

本文回顾国际上相关的都市永续发展指针系统,探讨指针系统架构与内涵之间的差异,进而说明永续台湾评量系统中"都市台湾"指针系统的重要特色,以及指针项目选定的理论基础.藉由永续台湾评量系统中22个范畴的型态辨识,本文分析了都市在台湾永续发展中所扮演的角色,确认都市生产与生活型态对于整体永续发展的重要性,也验证了生态系统观点中都市对于环境的依存与影响.考量都市发展决策过程需要永续性的评量机制,本文利用都市永续发展指针作为政策评估的重要变量,应用交互式计算机软件<感受性系统模型(Sensitivitv Model Tools)>以系统性观点进行都市发展政策永续性的评析.透过系统软件功能掌握变量结构的完整性以及系统变量之间的相互影响关系,并藉助于团体咨询与讨论的沟通方式建构半量化的(semi-quantitative)系统变量,配合概念化及模糊化的方式界定变量水准以及函数关系进行系统仿真工作.籍由系统仿真了解变量之间的互动关系,而政策评估案例分析显示台北市硬件建设政策较空间调整政策发挥较大的效果.永续指针与系统工具的结合整体而言提供了都市政策永续性评估的沟通与预测接口.     

Structural VAR and financial networks: A minimum distance approach to spatial modeling

In this paper, I interpret a time series spatial model (T-SAR) as a constrained structural vector autoregressive (SVAR) model. Based on these restrictions, I propose a minimum distance approach to estimate the (row-standardized) network matrix and the overall network influence parameter of the T-SAR from the SVAR estimates. I also develop a Wald-type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods as one possible identification strategy of SVAR models. Finally, I illustrate the methodology through an application to volatility spillovers across major stock markets using daily realized volatility data for 2004–2018.