Reverse-engineering country risk ratings: a combinatorial non-recursive model

The central objective of this paper is to develop a transparent, consistent, self-contained, and stable country risk rating model, closely approximating the country risk ratings provided by Standard and Poor’s (S&P). The model should be non-recursive, i.e., it should not rely on the previous years’ S&P ratings. The set of variables selected here includes not only economic-financial but also political variables. We propose a new model based on the novel combinatorial-logical technique of Logical Analysis of Data (which derives a new rating system only from the qualitative information representing pairwise comparisons of country riskiness). We also develop a method allowing to derive a rating system that has any desired level of granularity. The accuracy of the proposed model’s predictions, measured by its correlation coefficients with the S&P ratings, and confirmed by k-folding cross-validation, exceeds 95%. The stability of the constructed non-recursive model is shown in three ways: by the correlation of the predictions with those of other agencies (Moody’s and The Institutional Investor), by predicting 1999 ratings using the non-recursive model derived from the 1998 dataset applied to the 1999 data, and by successfully predicting the ratings of several previously non-rated countries. This study provides new insights on the importance of variables by supporting the necessity of including in the analysis, in addition to economic variables, also political variables (in particular “political stability”), and by identifying “financial depth and efficiency” as a new critical factor in assessing country risk.     

Interrelationships among the elements of national innovation systems: A statistical evaluation

An obstacle often faced by countries in attempting to improve the standard of living of their people and the productivity of their economic base is a low level of technological development. At the national level, such a problem may be conceptualized in the form of a National Innovative System (NIS), which represents a country's involvement in innovative activity. It is the purpose of this paper to develop a comprehensive NIS model through the identification of elements which characterize a country's NIS and of the interrelationships embedded in each individual system. The point of departure is to treat the NIS as any other sector of the economy. As a result, its various elements are delineated according to their role within the NIS, as inputs, outputs, moderators or as a measure of productivity. The nature of the interrelationships is assessed through a structural equations model linking the various elements. It is shown that substantial commonalities exist among the basic elements of the various NISs, notwithstanding large differences in technology development strategies that exist across countries and the wide disparity between levels of development of each country in the sample.     

FDI企业选址与东道国环境规制博弈

建立了高进入壁垒和低进入壁垒下双寡头博弈模型,应用逆向求解法,得到了引资国环境政策是否改变和FDI流出国企业是否迁址的临界条件.结果表明:在高进入壁垒的产业中,由于东道国较高的进入壁垒,外国企业可能不迁址;但如果考虑FDI产生的正外部性且溢出效应较高,引资国可能降低环境规制水平以吸引FDI企业进入,但从环境的角度考虑,此时全球环境损害更大.在低进入壁垒产业中,东道国政府选择提高环境规制水平是其占优策略,对引资国和全球都是帕累托改善的.     

Selecting rows and columns for training support vector regression models with large retail datasets

Although support vector regression models are being used successfully in various applications, the size of the business datasets with millions of observations and thousands of variables makes training them difficult, if not impossible to solve. This paper introduces the Row and Column Selection Algorithm (ROCSA) to select a small but informative dataset for training support vector regression models with standard SVM tools. ROCSA uses ε-SVR models with L₁-norm regularization of the dual and primal variables for the row and column selection steps, respectively. The first step involves parallel processing of data chunks and selects a fraction of the original observations that are either representative of the pattern identified in the chunk, or represent those observations that do not fit the identified pattern. The column selection step dramatically reduces the number of variables and the multicolinearity in the dataset, increasing the interpretability of the resulting models and their ease of maintenance. Evaluated on six retail datasets from two countries and a publicly available research dataset, the reduced ROCSA training data improves the predictive accuracy on average by 39% compared with the original dataset when trained with standard SVM tools. Comparison with the ε SSVR method using reduced kernel technique shows similar performance improvement. Training a standard SVM tool with the ROCSA selected observations improves the predictive accuracy on average by 21% compared to the practical approach of random sampling.     

Evaluating foreign investment environment in China: A systematic approach

To successfully develop a region's or country's economy, policy-makers have to utilize foreign investments. To attract the right foreign investors, the host has to understand not only the investors, but more importantly its own investment environment. This article presents a practical method for evaluating investment environment from the viewpoint of a host region or country. A real Chinese case was analysed by applying this method. The evaluation method is recommended not only to China, but also to those countries having investment environments similar to China's.     

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey.     

Organization and efficiency in the international insurance industry: A cross-frontier analysis

This paper employs cross-frontier analysis, an innovative tool based on data envelopment analysis, to provide new insight into the relationship between organization and efficiency in international insurance markets. We are the first to empirically test the expense preference hypothesis and the efficient structure hypothesis in a large cross-country study. For this purpose, we consider 23,807 firm-years for 21 countries from northern America and the European Union—a dataset not previously analyzed in this context. We find evidence for the efficient structure hypothesis in selected market segments, but we find no evidence for the expense preference hypothesis. Our results provide insight into the competitiveness of stock and mutual insurers from different countries. At the country level, the results can be used to compare different insurance markets. Our findings are especially interesting for the strategic management of insurance companies as well as for regulators and boards of national insurance associations.     

Measuring the national competitiveness of Southeast Asian countries

National competitiveness is a measure of the relative ability of a nation to create and maintain an environment in which enterprises can compete so that the level of prosperity can be improved. This paper proposes a methodology for measuring the national competitiveness and uses the 10 Southeast Asian countries for illustration. The basic idea is to deconstruct the complicated concept of national competitiveness to measurable criteria. The observations (data) on the criteria are then aggregated according to their importance to obtain an index of national competitiveness. For data collected from questionnaire surveys, a calibration technique has been devised to alleviate bias due to personal prejudice. In data aggregation, the importance is expressed by both a priori weights and a posteriori weights. These two types of weights consistently show that Singapore, Malaysia, and Thailand have the highest national competitiveness, while Myanmar, Cambodia, and Laos are the least competitive countries. The performance of each country in every criteria measured also provides directions for these countries to make improvements and for investors to allocate resources.     

The training institution efficiency of the semiconductor institute programme in Taiwan—application of spatiotemporal ICA with DEA approach

Despite the fact that Taiwan’s high-tech industry has gradually secured a leading position in the world, enterprises in Taiwan have striven to strengthen their technical advancement by providing employees with various internal or external training programmes. These institutional training programmes are designed to sustain competitive advantage, enhance the quality of manpower and improve operational efficiency. Much literature assesses the efficiency of an internal training programme that is initiated by a firm, but only a little literature studies the efficiency of an external training programme that is led by a government. Various efficiency measurement tools, such as conventional statistical methods and non-parametric methods, have been successfully developed in the literature. Among these tools, the data envelopment analysis (DEA) approach is one of the most widely discussed. However, the DEA's capability to discriminate efficient decision-making units from inefficient decision-making units requires much improvement (Adler and Yazhemsky). In this paper, a two-stage approach of integrating spatiotemporal independent component analysis (stICA) and DEA is developed for efficiency measurement. stICA is used to search for latent source signals where no relevant signal mixture mechanisms are available; and DEA is used to measure the relative efficiencies of decision-making units (DMUs). We suggest using stICA first to extract the input variables for generating independent components (IC), then selecting the ICs representing the independent sources of input variables, and finally inputting the selected ICs as new variables in the DEA model. To find the effects of environmental variables on the estimated efficiency scores, the Tobit–Bayes (censored) regression is applied. A simulated dataset and the training institution dataset provided by the Semiconductor Institute in Taiwan is used for analysis. The empirical result shows that the proposed method can not only separate performance differences between the training institutions but also improve the discriminatory capability of the DEA's efficiency measurement. The study results can serve as a reference for training institutions wishing to enhance their training efficiency.     

Socio-economic determinants of HIV/AIDS pandemic and nations efficiencies

This paper examines the influence and direction of social and economic determinants of the HIV/AIDS global epidemic across nations and assesses each country’s efficiency in battling the pandemic. The initial dataset consisted of 151 countries with five dependent variables and 90 explanatory variables (reduced to 50 after extensive exploratory data analysis of missing value patterns, undesirable multi-colinearities and multivariate outliers).     

The utility of nonequilibrium statistical mechanics,specifically transport theory,for modeling cohort data

This article introduces a new case‐based density approach to modeling big data longitudinally, which uses ordinary differential equations and the linear advection partial differential equations (PDE) to treat macroscopic, dynamical change as a transport issue of aggregate cases across continuous time. The novelty of this approach comes from its unique data‐driven treatment of cases: which are K dimensional vectors; where the velocity vector for each case is computed according to its particular measurements on some set of empirically defined social, psychological, or biological variables. The three main strengths of this approach are its ability to: (1) translate the data driven, nonlinear trajectories of microscopic constituents (cases) into the linear movement of macroscopic trajectories, which take the form of densities; (2) detect the presence of multiple, complex steady state behaviors, including sinks, spiraling sources, saddles, periodic orbits, and attractor points; and (3) predict the motion of novel cases and time instances. To demonstrate the utility of this approach, we used it to model a recognized cohort dynamic: the longitudinal relationship between a country's per capita gross domestic product (GDP) and its longevity rates. Data for the model came from the widely used Gapminder dataset. Empirical results, including the strength of the model's fit and the novelty of its results (particularly on a topic of such extensive study) support the utility of our new approach. © 2014 Wiley Periodicals, Inc. Complexity 20: 45–57, 2015     

An integrative evaluation framework for intelligent decision support systems

Evaluation of the overall effectiveness of decision support systems (DSS) has been a research topic since the early 1980s. As artificial intelligence methods have been incorporated into systems to create intelligent decision support systems (IDSS), researchers have attempted to quantify the value of the additional capabilities. Despite the useful and relevant insights generated by previous research, existing evaluation methodologies offer only a fragmented and incomplete view of IDSS value and the contribution of its technical infrastructure. This paper proposes an integrative, multiple criteria IDSS evaluation framework through a model that links the decision value of an IDSS to both the outcome from, and process of, decision making and down to specific components of the IDSS. The proposed methodology provides the designer and developer specific guidance on the intelligent tools most useful for a specific user with a particular decision problem. The proposed framework is illustrated by evaluating an actual IDSS that coordinates management of urban infrastructures.     

Formal verification of tail distribution bounds in the HOL theorem prover

Tail distribution bounds play a major role in the estimation of failure probabilities in performance and reliability analysis of systems. They are usually estimated using Markov's and Chebyshev's inequalities, which represent tail distribution bounds for a random variable in terms of its mean or variance. This paper presents the formal verification of Markov's and Chebyshev's inequalities for discrete random variables using a higher‐order‐logic theorem prover. The paper also provides the formal verification of mean and variance relations for some of the widely used discrete random variables, such as Uniform(m), Bernoulli(p), Geometric(p) and Binomial(m, p) random variables. This infrastructure allows us to precisely reason about the tail distribution properties and thus turns out to be quite useful for the analysis of systems used in safety‐critical domains, such as space, medicine or transportation. For illustration purposes, we present the performance analysis of the coupon collector's problem, a well‐known commercially used algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

The Algebra of Estimation in Linear Econometric Systems∗

In econometrics it is common for variables to be related together in a set of linear, multilateral and causal interdependencies. This type of system generally has properties which are unsatisfactory for application of classical regression techniques. Consequently, alternative estimation methods have been developed. This paper explores the relations between several such methods in terms of symmetric idempotents of predetermined variables and their orthogonal complements. Generalizations of two‐ and three‐stage least squares and instrumental variables are considered, including Wicken's estimator.² The relative efficiencies of the estimators are also discussed.

     

Prediction of work-related accidents according to working conditions using support vector machines

Support vector machines (SVMs), which are a kind of statistical learning methods, were applied in this research work to predict occupational accidents with success. In the first place, semi-parametric principal component analysis (SPPCA) was used in order to perform a dimensional reduction, but no satisfactory results were obtained. Next, a dimensional reduction was carried out using an innovative and intelligent computing regression algorithm known as multivariate adaptive regression splines (MARS) model with good results. The variables selected as important by the previous MARS model were taken as input variables for a SVM model. This SVM technique was able to classify, according to their working conditions, those workers that have suffered a work-related accident in the last 12 months and those that have not. SVM technique does not over-fit the experimental data and gives place to a better performance than back-propagation neural network models. Finally, the results and conclusions of this study are presented.     

制度落差与中国跨国企业海外进入模式关系研究

本文分析了制度落差与中国企业海外市场进入模式之间的关系,着重对比了制度顺差与制度逆差情况下,制度差异对企业海外进入模式决策的影响。研究发现,在正式制度顺差的情况下,东道国与母国间正式制度差异越大,企业越倾向于选择以合资形式建立子公司;在正式制度逆差情况下,东道国与母国间正式制度差异越大,企业越倾向于选择以全资形式建立子公司。同时发现,正式制度落差并不影响非正式制度对进入模式的影响方向,即随着母国与东道国非正式制度差异的增大,企业更倾向于以全资形式建立子公司。     

Predicting online-purchasing behaviour

This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson's global score search algorithm to find the best subset of predictors. We contribute to the literature by using variables from four different categories in predicting online-purchasing behaviour: (1) general clickstream behaviour at the level of the visit, (2) more detailed clickstream information, (3) customer demographics, and (4) historical purchase behaviour. The results show that predictors from all four categories are retained in the final (best subset) solution indicating that clickstream behaviour is important when determining the tendency to buy. We clearly indicate the contribution in predictive power of variables that were never used before in online purchasing studies. Detailed clickstream variables are the most important ones in classifying customers according to their online purchase behaviour. Though our dataset is limited in size, we are able to highlight the advantage of e-commerce retailers of being able to capture an elaborate list of customer information.     

A locally linear neuro-fuzzy model for supplier selection in cosmetics industry

Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques.     

Sparse Multivariate Regression With Covariance Estimation

We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. This method, which we call multivariate regression with covariance estimation (MRCE), involves penalized likelihood with simultaneous estimation of the regression coefficients and the covariance structure. An efficient optimization algorithm and a fast approximation are developed for computing MRCE. Using simulation studies, we show that the proposed method outperforms relevant competitors when the responses are highly correlated. We also apply the new method to a finance example on predicting asset returns. An R-package containing this dataset and code for computing MRCE and its approximation are available online.