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.     

基于结构方程的北京居民幸福指数因素分析

影响人们幸福感的因素是众多的,有可以直接观察和测量的显变量,也有不易直接观测和测量的隐变量。本文利用结构方程模型,通过路径分析,测定了影响北京市居民幸福感的五个隐变量及其影响强度,分别为身心健康（0．53）、物质条件（0．509）、家庭生活（0．298）、社会幸福感（0．143）和自我价值的实现（0．014）。     

初选阶段知识联盟成员评价与选择模型

根据知识联盟成员选择与形成的实际过程,将知识联盟成员的选择分为初选和谈判(精选)两个阶段。在综合国外联盟实践经验及影响联盟成败的实证性研究结论的基础上,分析了知识联盟成员初选阶段影响联盟成败的关键变量,以此构建了知识联盟成员初选模型,并依据指标间的相互关系,提出了基于二值变量的初选阶段评价方法。     

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.     

Digital divide and financial development in Africa

This paper analyses the influence of the digital divide on the new IMF financial development index on a panel of 34 African countries over the period 2005–2017. Using the instrumental variables technique, we arrive at the main result that ICT divide is a severe handicap for the financial systems development in Africa. The use of financial development sub-indices relating to financial institutions and financial markets, as well as their dimensions confirms the negative effect of the digital divide. Our result remains stable when we use alternative measures of financial development and ICT indicators. Furthermore, we found that the digital divide between countries is also a severe handicap for the financial development of countries lagging behind. On the other hand, countries with a technological lead or a digital dividend have relatively developed financial systems. Some recommendations have been suggested to promote digital penetration in Africa, as well as to improve the receptivity and flexibility of African financial systems.     

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.