The conditional probability and the linear flow graph approaches to analyzing probabilistic causal systems

An alternative theoretical approach to the analysis for dichotomous causal systems that involve probabilistic causation, the conditional probability approach, has recently been explicated. It was shown that there exist various composition and decomposition rules for analyzing various kinds of general causal systems, and an important distinction between pure-“or”-and pure-“and”-causal systems was explicated. In this paper these earlier results are used to analyze a causal system which has been studied by J.A. Davis (1976), who uses his linear flow graph approach to analysis. The results of the conditional probability approach are compared to the linear flow graph, and it is shown that the two approaches lead to strikingly different results.     

A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model

Computationally efficient methods for Bayesian analysis of seemingly unrelated regression (SUR) models are described and applied that involve the use of a direct Monte Carlo (DMC) approach to calculate Bayesian estimation and prediction results using diffuse or informative priors. This DMC approach is employed to compute Bayesian marginal posterior densities, moments, intervals and other quantities, using data simulated from known models and also using data from an empirical example involving firms’ sales. The results obtained by the DMC approach are compared to those yielded by the use of a Markov Chain Monte Carlo (MCMC) approach. It is concluded from these comparisons that the DMC approach is worthwhile and applicable to many SUR and other problems.     

Estimating causal effects of credit decisions

In principle, making credit decisions under uncertainty can be approached by estimating the potential future outcomes that will result from the various decision alternatives. In practice, estimation difficulties may arise as a result of selection bias and limited historic testing. We review some theoretical results and practical estimation tools from observation study design and causal modeling, and evaluate their relevance to credit decision problems. Building on these results and tools, we propose a novel approach for estimating potential outcomes for credit decisions with multiple alternatives based on matching on multiple propensity scores. We demonstrate the approach and discuss results for risk-based pricing and credit line increase problems. Among the strengths of our approach are its transparency about data support for the estimates and its ability to incorporate prior knowledge in the extrapolative inference of treatment-response curves.     

交叉熵方法理论上的缺陷及其改进研究

本文针对传统交叉熵（CE）方法在平衡和更新社会核算矩阵（SAM）研究中存在的缺陷进行分析,提出了基于离差熵绝对值期望最小化的改进模型1和基于离差熵平方期望最小化的改进模型2。理论结果表明：传统的CE方法得到的解是本文所提出的基于离差熵绝对值期望最小化改进模型1的一种特殊情况。同时为检验模型改进前后的实际效果,本文运用蒙特卡罗方法进行模拟分析,模拟结果表明：改进后的模型2得到的SAM流量矩阵精度比传统的CE方法得到的精度更高,而改进后的模型1得到的精度却相对较低。     

The “management‐per‐result” approach to international development project design

Results‐based management (RBM) has proved to be a valuable tool for international development project management; however, there are some inconsistencies that limit the use of RBM at the design phase to manage for results. This article presents a “management‐per‐result” approach to reinforcing the project design function of RBM and illustrates its application to a real‐life project. Shying away from a technocratic approach, it emphasizes a “quick‐and‐dirty” approach and proposes an updated version of the logical framework to include success criteria and factors and very rough estimates for both project costs and benefits for targeted project results for different types of projects (infrastructure development, “process” type of project, and so forth).     

Hedonic prices,demands for urban housing amenities,and benefit estimates

This paper uses a Rosen, two-step, hedonic price-trait demand approach to estimate demand functions for a vector of urban amenities. To ascertain whether this theoretically preferred approach yields benefit estimates which differ from the oft-used Ridker-Henning, one-step, hedonic approach we conduct a sensitivity analysis. We find that the two-step approach does yield different benefit estimates and that the differences are large for some amenities. The estimates are sensitive to the functional form of the hedonic equation when the forms are significantly different according to modified Box-Cox results, but are not particularly sensitive to specification of the amenity demand equation.     

Testing random assignment to peer groups

Identification of peer effects is complicated by the fact that the individuals under study may select their peers. Random assignment to peer groups has proven useful to sidestep such a concern. In the absence of a formal randomization mechanism, it needs to be argued that assignment is “as good as” random. This paper introduces a simple yet powerful test to do so. We provide theoretical results for this test. As a by-product, we equally obtain such results for an approach popularized by Guryan et al. (2009). These results help to explain why this approach suffers from low power, as has been observed elsewhere.     

Developing a multivariate approach to monitor fuzzy quality profiles

Referring to several applications in which the response quality characteristic is fuzzy, this paper studies how the profile functional relationship between a fuzzy response variable and a predictor variable can be monitored by using a fuzzy regression model which is referred to as profile. The purpose of this paper is to develop a multivariate approach for monitoring process/product fuzzy quality profiles in phase I for applications where the quality characteristic is linguistic, imprecise, vague or deficient. The multivariate approach includes three fuzzy multivariate control charts which are developed by using fuzzy set theory to monitor fuzzy profiles in order to achieve an in-control process. The performance of developed approach is investigated on the basis of signal probability in various out-of-control scenarios through a simulation study. Compared with univariate approach, the results indicate a good performance of our multivariate approach in detecting all sized shifts in process profiles. A real case in tourism industry is utilized to show the applicability of the proposed approach.     

CLOE: a cross-lingual ontology enrichment using multi-agent architecture

ABSTRACT

This article proposes across-lingual ontology enrichment (CLOE) approach for enriching ontologies from amultilingual text or other ontologies. Athree-level hierarchical multi-agent model has been built, where each node is modelled as an agent. This has several advantages: ontologies can be used to enrich other ontologies in adifferent language, several ontologies can be enriched at the same time, and domain-independent. A prototype for the proposed approach has been implemented. Evaluation results are promising, showing considerable precision and recall scores. We believe that this approach is the first such attempt to enrich multiple ontologies using adomain-independent approach.     

仓储系统设计的实验教学模式研究

在分析仓储系统设计的实验教学目标的前提下,提出实验教学中可行的仓储系统设计步骤。同时研究仓储系统实验教学内容与组织过程———合理组织教学内容、严格实验过程指导、科学实验考核;最后按实验步骤进行仓储系统的方案设计。     

Portfolio selection in a data-rich environment

We model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables. This approach (hereafter diffusion index approach) offers the opportunity to exploit a much richer information base to improve portfolio selection. We use factor analysis to estimate the space spanned by the factors. This provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity. We consider an empirical application to illustrate the practical usefulness of our approach. The results indicate that the diffusion index approach helps to improve the portfolio performance.     

A two-step hybrid investment strategy for pension funds

We propose a two-step hybrid investment strategy suitable for pension funds. Our method consists of an active component (an optimization-based approach to decide the asset allocation), followed by a passive strategy (an index-based approach). We test our strategy with data from the Chilean pension system using two different risk metrics and we show that our approach, in three out of five cases, yields results that are better than those generated by the Chilean fund administrators. In the two cases where our approach underperformed we show that it was the result of excessively tight constraints set up by the regulator.     

Calibrating longitudinal models of residential mobility and migration: an assessment of a non-parametric marginal likelihood approach

"This paper discusses the problems of controlling for omitted variables in estimating the structural parameters of longitudinal models and focuses upon an assessment of a non-parametric marginal maximum likelihood approach suggested by the results of Laird....The approach is shown to be statistically valid for a plausible discrete-time model of the incidence of residential or migration moves, at least for data in which no household moves in every time period. Empirical evaluation with two large [U.S.] datasets on residential mobility indicates that the approach is also computationally feasible and provides a promising alternative to more conventional methods for controlling for omitted variables."     

On solving bias‐corrected non‐linear estimation equations with an application to the dynamic linear model

In a seminal paper, Mak, Journal of the Royal Statistical Society B, 55, 1993, 945, derived an efficient algorithm for solving non‐linear unbiased estimation equations. In this paper, we show that when Mak's algorithm is applied to biased estimation equations, it results in the estimates that would come from solving a bias‐corrected estimation equation, making it a consistent estimator if regularity conditions hold. In addition, the properties that Mak established for his algorithm also apply in the case of biased estimation equations but for estimates from the bias‐corrected equations. The marginal likelihood estimator is obtained when the approach is applied to both maximum likelihood and least squares estimation of the covariance matrix parameters in the general linear regression model. The new approach results in two new estimators when applied to the profile and marginal likelihood functions for estimating the lagged dependent variable coefficient in the dynamic linear regression model. Monte Carlo simulation results show the new approach leads to a better estimator when applied to the standard profile likelihood. It is therefore recommended for situations in which standard estimators are known to be biased.     

The Game Form Approach to Redistribution

This paper reviews the game form approach to redistributive policies. This approach provides a justification for studying tax systems as good institutional arrangements for reallocating resources in a heterogeneous society. After situating the problem of asymmetric information for public policy, the paper provides a short overview of the main concepts and results in implementation theory. Next, these tools are used to inquire about the possibility to decentralise the reallocation of resources in a production economy with the help of tax/transfer systems. Finally, the paper deals with some recent extensions of the theory. A simple example is used throughout to illustrate the different results.     

The effects of the gamified flipped classroom environment (GFCE) on students’ motivation,learning achievements and perception in a physics course

Increasing students’ motivation for classes and learning has been a critical research topic in the flipped classroom approach. A gamified flipped classroom environment is proposed in this study. The objective of the study is to enhance students’ motivations, learning achievements and their perceptions of the designed environment. In the study, a true experimental design was used. The study was conducted with 61 undergraduate students taking a Physics-2 course. The experimental group students learned in the gamified flipped classroom environment, while the control group students learned with the flipped classroom approach without a gamification strategy. Data were collected from a physics motivation questionnaire, electromagnetism achievement test and semi structured interviews. The experimental results indicate that the students from the experimental group had a significantly increased motivation for the classroom and learning achievements in comparison to the students in the control group. Moreover, it has been determined that the students’ opinions on the gamified classroom environment were positive. Additionally, the students interviewed stated that the approach had positive effects on their motivation for physics class. The results of this study can assist lecturers and instructors in integrating the gamification strategy to the flipped classroom approach.     

Creditworthiness evaluation of Italian SMEs at the beginning of the 2007–2008 crisis: An MCDA approach

In this paper we use a Multi-Criteria Decision Analysis (MCDA) approach in order to evaluate the creditworthiness of about 40,000 Italian Small and Medium-sized Enterprises (SMEs) viewed as potential applicants for bank loans in the three-year period 2006–2008, a timespan embracing the beginning phase of the recent economic and financial crisis. This approach is able to consider simultaneously different factors affecting the firms’ solvency level. The results obtained in terms of classification into homogeneous rating classes, scoring and migration probabilities show that the proposed approach is able to unveil early signal of recession in the Italian SMEs sector. Furthermore, some comparisons between our approach and a couple of known methodologies for creditworthiness assessment show the goodness of our method.     

Decisionmetrics: A decision-based approach to econometric modelling

An approach to developing a possibly misspecified econometric model that will be used as the beliefs of an expected utility maximiser is proposed. This approach builds on a novel objective function that measures the value of predictive distributions in decision-making and is used in model estimation, selection and evaluation. The methods proposed also provide an econometric approach for developing arbitrary parametric action rules such as technical trading rules. The approach is compared in detail with existing methods and is applied in the context of a CARA investor's decision problem where analytical and empirical results suggest it is very effective.     

Inference on Reliability in Two-parameter Exponential Stress–strength Model

The problem of hypothesis testing and interval estimation of the reliability parameter in a stress–strength model involving two-parameter exponential distributions is considered. Test and interval estimation procedures based on the generalized variable approach are given. Statistical properties of the generalized variable approach and an asymptotic method are evaluated by Monte Carlo simulation. Simulation studies show that the proposed generalized variable approach is satisfactory for practical applications while the asymptotic approach is not satisfactory even for large samples. The results are illustrated using simulated data.     

Escape dynamics: A continuous-time approximation

We extend a continuous-time approximation approach to the analysis of escape dynamics in economic models with constant gain adaptive learning. This approach is based on the application of the results of continuous-time version of large deviations theory to the linear diffusion approximation of the original discrete-time dynamics under learning. We characterize escape dynamics by analytically deriving the most probable escape point and mean escape time. The approximation is tested on the Phelps problem of a government controlling inflation while adaptively learning a misspecified Phillips curve, studied previously by Sargent (1999) and Cho et al. (2002) (henceforth, CWS), among others. We compare our results with simulations extended to very low values of the constant gain and show that, for the lowest gains, our approach approximates simulations relatively well. We express reservations regarding the applicability of any approach based on large deviations theory to characterizing escape dynamics for economically plausible values of constant gain in the model of CWS when escapes are not rare. We show that for these values of the gain it is possible to derive first passage times for learning dynamics reduced to one dimension without resort to large deviations theory. This procedure delivers mean escape time results that fit the simulations closely. We explain inapplicability of large deviations theory by insufficient averaging near the point of self-confirming equilibrium for relatively large gains which makes escapes relatively frequent, suggest the changes which might help approaches based on the theory to work better in this gain interval, and describe a simple heuristic method for determining the range of constant gain values for which large deviations theory could be applicable.