A Probabilistic Clustering Model for Variables of Mixed Type

This paper develops a probabilistic clustering model for mixeddata. The model allows analysis of variables of mixed type: thevariables may be nominal, ordinal and/or quantitative. The modelcontains the well-known models of latent class analysis as submodels.As in latent class analysis, local independence of the variables isassumed. The parameters of the model are estimated by the EMalgorithm. Test statistics and goodness-of-fit measures are proposedfor model selection. Two artificial data sets show the usefulness ofthese tests. An empirical example completes the presentation.     

Equilibrium prices in a random exchange economy with dependent agents

Summary. This paper presents a model of a Walrasian exchange economy in which the preferences and endowments of the agents are random. Stochastic interaction among the agents is formally described in terms of dependency neighborhoods. The main result is a characterization of the distribution of market-clearing prices in a large economy. Received: April 23, 1999; revised version: May 6, 1999     

Discrepant Values and Measures of Negotiator Performance

The thesis of the paper is that measuring negotiator performance correctly is difficult because the values that those of us who measure negotiator performance think negotiators are maximizing may differ from the values negotiators are actually maximizing. When such discrepant values exist, using performance measures that do not account for them can lead easily to incorrect conclusions about negotiator performance. Indeed, good performance may be judged poor, and vice-versa. This paper explores several related literatures, including the experimental-bargaining, behavioral-decision-making, and procedural-justice literatures, to demonstrate that discrepant values exist. It then demonstrates that whenever performance measures are used as dependent variables in negotiation experiments, the existence of discrepant values can lead to both Type I and Type II construct-validity errors.     

我国软件外包承接城市效率评价与驱动因素研究

就 业问题、环境污染以及从业人员研发能力下降已经成为制约软件外包承接城市发展的瓶颈问 题。文章运用DEA方法构建考虑非期望产出和非合意变量的软件外包承接效率评价模型,重 点比较不同模型下我国软件外包承接城市效率的差异,并引入Tobit回归分析软件外包承接 城市效率的驱动因素。研究发现：我国软件外包承接城市的投入产出组合在逐步优化,且东 部沿海城市的效率值明显优于中西部城市,其中大连市的效率值始终为1,处于生产的前沿 面。另外,人才竞争力对软件外包承接城市效率有显著的正向影响,信息技术基础设施竞争力与承接效率的关系呈显著负相关。     

Does computer penetration increase farmers’ income? An empirical study from China

The diffusion and adoption of modern information technology provide new chance for China to close urban-rural income gap. This paper uses China's provincial panel data from 2002 to 2013 to investigate the effect of computer penetration on rural residents' income. A public program aiming to connect every village with broadband Internet and other rural facilities provides plausibly exogenous variation in rural residents' availability and adoption of the broadband Internet, which is used to explore the instrument variable for rural computer penetration. The results show that rural computer penetration tends to increase rural residents' income over time, but the average effect remains limited. The dynamic panel threshold effects model, which allows for both the threshold variable and other covariates to be endogenous, is further used to explore the constraints of the income-increase effect of rural computer penetration. It shows that the effect is at least doubled over the average effect estimated from instrument variables method, once the digital divide causes are removed. Our findings have important implications for the government to increase rural residents' income and reduce urban-rural income gap by encouraging rural computer usage and removing the digital divide.     

A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia

Data envelopment analysis (DEA) is generally used to evaluate past performance and multi objective linear programming (MOLP) is often used to plan for future performance goals. In this study, we establish an equivalence relationship between MOLP problems and combined-oriented DEA models using a direction distance function designed to account for desirable and undesirable inputs and outputs together with uncontrollable variables. This equivalence model can be effectively used to support interactive processes and performance measures designed to establish future performance goals while taking into account the preferences of decision makers (DMs). In particular, it allows DMs to consider different efficiency improvement strategies when subject to budgetary restrictions. The applicability of the proposed method and the efficacy of the procedures and algorithms are demonstrated using a case study where the performance of high schools in the City of Philadelphia is evaluated.     

The effects of population density on subjective well-being: A case-study of Slovakia

The main objective of the study is to examine the effects of population density on individuals’ subjective well-being (SWB). The physical characteristics of the built environment have recently received more attention regarding their role in influencing well-being. The research is based on two waves of a representative survey EU-SILC (2013: N = 12,791 and 2018: N = 14,665). The study focuses on the emotional domain of SWB and uses a score based on the Mental Health Inventory (MHI-5). In order to remove the potential problem of reverse causality, a two-stage least squares regression model with instrumental variables is used. It shows that population density positively affects the SWB score. The results reported in this study can be useful for urban planning aimed at optimizing spatial structure while taking into account the factors which positively affect subjective well-being. The findings can also be beneficial for assessing the resilience and vulnerability of cities.     

Forecasting stock market volatility: The role of technical variables

Accurate volatility forecasts are required by both market participants and policy makers. In this paper, we forecast stock return volatility by using a wide range of technical indicators constructed based on the past behavior of stock price, volatility and trading volume. Our out-of-sample results indicate that the incorporation of technical variables in the autoregression benchmark can produce significantly more accurate volatility forecasts. The forecasting performance of the combination of technical indicators is further compared with that of the popular economic indicators. Technical variables perform better than economic variables when the economy is an expansion, while the economic variables generate more accurate forecasts when the economy belongs a recession. These two types of variables provide complementary information over the business cycle. We obtain more reliable forecasts by combining all economic and technical information together than by combining either type of information alone.     

Human staff vs. service robot vs. fellow customer: Does it matter who helps your customer following a service failure incident?

The key to excellent service delivery is to ensure harmonious interactions between service actors. Therefore, in the event of service failure incidents, an understanding of the roles and interactions of these service actors is critical to achieving positive service outcomes. This research addresses the question: How do interactions between customers and these service actors (human staff/automated technological interface/fellow customer) influence their service experience following a service failure? We draw on role theory and answer this question via three experimental studies. Findings show that customers evaluate their service experience less favourably when receiving service recovery from fellow customers rather than firms (human staff and service robots). Furthermore, a firm’s instrumental recovery, rather than informational recovery, leads to a more favourable service evaluation, although this effect is absent when service recovery was given by fellow customers. Findings provide insights into the role of each actor in determining customer service evaluation.     

Structural analysis with mixed-frequency data: A model of US capital flows

We develop a new structural Vector Autoregressive (SVAR) model for analysis with mixed-frequency data. The MIDAS-SVAR model allows to identify structural dynamic links exploiting the information contained in variables sampled at different frequencies. It also provides a general framework to test homogeneous frequency-based representations versus mixed-frequency data models. A set of Monte Carlo experiments suggests that the test performs well both in terms of size and power. The MIDAS-SVAR is then used to study how monetary policy and financial uncertainty impact on the dynamics of gross capital inflows to the US. While no relation is found when using standard quarterly data, mixed frequency analysis exploiting the variability present in the series within the quarter shows that the effect of an interest rate shock is greater the longer the time lag between the month of the shock and the end of the quarter.