社会核算矩阵平衡方法研究——最小二乘交叉熵法

针对初始不平衡SAM与真实SAM关系未知的情形,本文提出了最小二乘交叉熵(LSCE)平衡法。基于最小二乘法(LS)、交叉熵法(CE)以及LSCE方法的仿真分析表明,CE与LS的相对稳健性取决于初始不平衡SAM的误差特征:当初始不平衡SAM的交易流量更接近于真实SAM时,LS较优;当初始不平衡SAM的系数矩阵更接近于真实SAM时,CE较优。LSCE方法同时考虑了SAM表流量和系数矩阵信息,故可得到精度介于LS和CE间的平衡SAM表,从而保证了平衡后SAM表的相对精度。     

Estimation of Non-Stationary Social Accounting Matrix Coefficients with Supply-Side Information

Given aggregated data, a framework for estimating the entries of a social accounting matrix (SAM), or any large matrix of expenditures, trade or income flows, is developed. Under this framework it is possible to evaluate the contribution of structural and supply-side information, as well as policy variables, within the generalized context of a non-stationary SAM. Inference and diagnostic properties are developed as well. This new estimator can be viewed as a generalized maximum likelihood estimator. Stationary and non-stationary estimates of the US SAM for the years 1987-1994 together with the effects of supply-side variables are analyzed.     

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。     

Default estimation,correlated defaults,and expert information

Capital allocation decisions are made on the basis of an assessment of creditworthiness. Default is a rare event for most segments of a bank's portfolio and data information can be minimal. Inference about default rates is essential for efficient capital allocation, for risk management and for compliance with the requirements of the Basel II rules on capital standards for banks. Expert information is crucial in inference about defaults. A Bayesian approach is proposed and illustrated using prior distributions assessed from industry experts. A maximum entropy approach is used to represent expert information. The binomial model, most common in applications, is extended to allow correlated defaults yet remain consistent with Basel II. The application shows that probabilistic information can be elicited from experts and econometric methods can be useful even when data information is sparse. Copyright © 2009 John Wiley & Sons, Ltd.     

Projection‐Based Inference in Randomised Clinical Trials

Covariate information is often available in randomised clinical trials for each subject prior to treatment assignment and is commonly utilised to make covariate adjustment for baseline characteristics predictive of the outcome in order to increase precision and improve power in the detection of a treatment effect. Motivated by a nonparametric covariance analysis, we study a projection approach to making objective covariate adjustment in randomised clinical trials on the basis of two unbiased estimating functions that decouple the outcome and covariate data. The proposed projection approach extends a weighted least‐squares procedure by projecting one of the estimating functions onto the linear subspace spanned by the other estimating function that is E‐ancillary for the average treatment effect. Compared with the weighted least‐squares method, the projection method allows for objective inference on the average treatment effect by exploiting the treatment specific covariate–outcome associations. The resulting projection‐based estimator of the average treatment effect is asymptotically efficient when the treatment‐specific working regression models are correctly specified and is asymptotically more efficient than other existing competitors when the treatment‐specific working regression models are misspecified. The proposed projection method is illustrated by an analysis of data from an HIV clinical trial. In a simulation study, we show that the proposed projection method compares favourably with its competitors in finite samples.     

Supplier evaluation in the context of circular economy: A forward step for resilient business and environment concern

Resource depletion is a concern for the global economy; many think that available resources on the planet will not be able to cater to an ever-growing population. Thus, economies are trying to become circular, leaving behind the linear tradition linear approach. In the circular economy (CE), physical resources and energy are made to loop back into the supply chain (SC) for a more extended period. Proper selection of suppliers is an essential criterion towards proper execution of the CE principle in SC. In this research, we have constructed a framework for evaluating the supplier concerning the CE implementation. Further, this research identifies the criterion and sub-criterion, which are pertinent for evaluating the supplier in CE context. Fuzzy-based ‘Criteria Importance Through Inter-Criteria Correlation (CRITIC)’ method is justifiably applied to determine the aggregated weights of the criteria. Finally, ‘Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)’ method is used to determine the suppliers' ranking in the Indian automobile industry. Six criteria and 24 sub-criteria are obtained as per recent literature and then inputs from experts. ‘Environment’ criterion came out as the most favourable criterion with a subjective weight of 0.230. The current research is one of the first such attempts to provide criteria for supplier selection in a CE environment. The developed framework would help organisations in implementing CE-based supplier selection. The identified criteria and sub-criteria would provide organisations with means to evaluate suppliers and help suppliers develop an effective and efficient CE based on the SC.     

Bayesian estimation of transition probabilities from repeated cross sections

This paper discusses some simple practical advantages of Markov chain Monte Carlo (MCMC) methods in estimating entry and exit transition probabilities from repeated independent surveys. Simulated data are used to illustrate the usefulness of MCMC methods when the likelihood function has multiple local maxima. Actual data on the evaluation of an HIV prevention intervention program among drug users are used to demonstrate the advantage of using prior information to enhance parameter identificaiton. The latter example also demonstrates an important strength of the MCMC approach, namely the ability to make inferences on arbitrary functions of model parameters.     

Food-For-Work versus Cash-For-Work: Emergency Assistance in Palestine

In this paper we analyse the provision of emergency assistance (food assistance, cash transfers, employment programmes, etc) to a country whose economy has been decimated since the start of the second intifada. We try to simulate the different potential effects brought about by these different policies and, in particular, to draw some policy implications concerning the Food-for-Work versus Cash-for-Work debate. To that end we have constructed a general equilibrium model of the Palestinian economy that we calibrate on the (pre-intifada) Social Accounting Matrix (SAM) of 1998. We give a so-called 'intifada-shock' to construct a counterfactual 'post-intifada' SAM which serves as basis for our policy simulations. We show that monetary aid from abroad is to be preferred to food aid from abroad. We argue that a labour-oriented approach (subsidizing the most labour-intensive sectors) is to be preferred to a welfare-oriented approach where the subsidized sectors produce those goods that dominate the consumption basket.     

Reevaluating waste as a resource under a circular economy approach from a system perspective: Findings from a case study

The circular economy (CE), definable as a system focused on the reorganization of material, information, and energy flows to achieve greater resource efficiency through the reuse, remanufacturing, and recycling of materials, is a concept widely discussed by practitioners and scientists of many disciplines. Waste recycling is integral to the CE, but there are still few articles focused on waste, and only few studies shed light on CE implementation at the company level. This paper focuses on a particular type of waste, that is, absorbent hygiene products (AHPs), which represents a nonnegligible fraction of municipal solid waste, considered an increasingly serious global challenge. We conducted our analysis on FaterSMART, an Italian firm that developed a unique worldwide technology able to totally convert AHP raw material wastes into recyclable materials, under a CE approach. The case study findings are based upon semi-structured interviews, direct observations, and analysis of FaterSMART's archival documents and are analyzed according to a framework developed for the research and focused on the place of waste from a linear economy, in which waste is considered a burden to CE, in which waste is considered a resource. The latter case is what we found that happens at FaterSMART. FaterSMART's findings could contribute to open up new management scenarios and stimulate further research into how this and similar types of technology will help societies to change from the “use-it-once-and-throw-it away” mentality of linear business models to the sustainable CE model that fully conceptualize waste as a resource for the system.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

Implementing circular economy in the textile and clothing industry

This research reveals the current state of the circular economy (CE), challenges and opportunities of implementing CE and interventions that could facilitate effective implementation of CE in the textile and clothing (TC) industry. The study uses a survey method within 114 TC companies based in Bangladesh, Vietnam and India revealing the correlation of CE fields of action (take, make, distribute, use and recover) with sustainability (economic, environmental and social) performance. The lack of financial, technological and human resources along with management's reluctance and end-user's indifference to sustainability is the biggest challenge for CE implementation. The research further derives that the TC firms are unable to eradicate the challenges to CE implementation without a holistic approach that involves the collective effort from the industry, host government's incentives, their buyers and above all the conscience of the end-users. Finally, the study reveals that the collaborative efforts, knowledge sharing in sustainability management across the value chain and marketisation of the waste recycling, among others, are a few actions the stakeholders of the TC industry must adopt for implementing CE successfully.     

Missing links in key sector analysis

In general terms, key sectors analysis aims at identifying and quantifying the economic impact of a sector in a given economy. For a sector, we mean here either an industry or a region, or even a cluster of them. Quite a few measures and methodologies of varied complexity have been proposed in the literature, from multiplier sums to extraction methods, but not without debate about their properties and information content. All of them, to our knowledge, focus exclusively on the interdependence effects that result from an input–output structure of the economy. By so doing the approach misses critical links beyond the interindustry ones. A productive sector's role is that of producing but also that of generating and distributing income among primary factors and households as a result of production. Thus, when measuring a sector's role, the income generating process should not be omitted if we want to elucidate the sector's true economic impact. A simple way to make the missing income links explicit is to use the SAM (Social Accounting Matrix) facility. Extending an extraction methodology to the SAM we compare lost output with and without the missing links. We observe that substantial differences in sectoral lost gross output arise but, even more important, we capture the implied shifting in the rank ordering of sectors.     

The transition from linear economy to circular economy for sustainability among SMEs: A study on prospects,impediments, and prerequisites

The circular economy (CE) is a more holistic approach that advocates towards extracting the value from the waste and reaching sustainability goals. The objective of the present study is to highlight the prospects, impediments, and prerequisites while transiting from the linear economy (LE) to CE of SMEs. The study gathers information on prospects, impediments, and prerequisites for the transition of LE to a CE from recent studies . A semi-structured interview questionnaire was prepared, and a survey was conducted on representatives of six SMEs . Further, six caselets were developed to understand the prospects, impediments, and prerequisites based on the findings of the interview and previous information gained from existing literature . The major prospects favoring transition from LE to CE found in the study are significance of 3R (reduce and reuse and recycling) approach, CE leads to competitive advantage, recycling attracts consumers in few cases, CE helps in achieving sustainability goals and reuse of materials are significant in resource conservation. There are certain impediments found such as issues associated with awareness, recyclability issues, financial challenges, and weak management vision of SMEs towards CE implementation. Other resource-based impediments were found related to trained employees, lack of experience. Whereas, consumer acceptability is also a major concern towards implementing CE. The findings of the study suggest major prerequisites towards CE implementations such as strong “management will,” innovation, technology up-gradation, training to employees, motivation, and appropriate guidelines. Government pressure to implement CE cannot be an effective step towards the transition of LE to CE.     

A new structural break model,with an application to Canadian inflation forecasting

This paper develops an efficient approach to modelling and forecasting time series data with an unknown number of change-points. Using a conjugate prior and conditioning on time-invariant parameters, the predictive density and the posterior distribution of the change-points have closed forms. Furthermore, the conjugate prior is modeled as hierarchical in order to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients, or both. The regime duration can be modelled as a Poisson distribution. A new, efficient Markov chain Monte Carlo sampler draws the parameters from the posterior distribution as one block. An application to a Canadian inflation series shows the gains in forecasting precision that our model provides.     

Bayesian analysis of the error correction model

This paper presents a method for estimating the posterior probability density of the cointegrating rank of a multivariate error correction model. A second contribution is the careful elicitation of the prior for the cointegrating vectors derived from a prior on the cointegrating space. This prior obtains naturally from treating the cointegrating space as the parameter of interest in inference and overcomes problems previously encountered in Bayesian cointegration analysis. Using this new prior and Laplace approximation, an estimator for the posterior probability of the rank is given. The approach performs well compared with information criteria in Monte Carlo experiments.     

Evaluating the FLQ and AFLQ formulae for estimating regional input coefficients: empirical evidence for the province of Córdoba,Argentina

This paper uses survey-based data of the Argentinian province of Córdoba to conduct an empirical test of the performance of the Flegg's location quotient (FLQ) and augmented FLQ (AFLQ) formulae for estimating regional input coefficients. A comparison is made with conventional methods based on location quotients. The possibility of using prior information about the extent of self-sufficiency of particular sectors is explored. The empirical work employs a range of statistical criteria with contrasting properties, and examines performance in terms of each method's ability to estimate regional input coefficients, output multipliers and imports. Particular attention is paid to the problem of choosing a value for the unknown parameter δ in the FLQ and AFLQ formulae. These formulae are found to give the best overall results of the non-survey methods considered in the paper. However, the AFLQ typically produces slightly more accurate results than the FLQ, in line with the findings of previous studies.     

Partial identification of probability distributions with misclassified data

This paper addresses the problem of data errors in discrete variables. When data errors occur, the observed variable is a misclassified version of the variable of interest, whose distribution is not identified. Inferential problems caused by data errors have been conceptualized through convolution and mixture models. This paper introduces the direct misclassification approach. The approach is based on the observation that in the presence of classification errors, the relation between the distribution of the ‘true’ but unobservable variable and its misclassified representation is given by a linear system of simultaneous equations, in which the coefficient matrix is the matrix of misclassification probabilities. Formalizing the problem in these terms allows one to incorporate any prior information into the analysis through sets of restrictions on the matrix of misclassification probabilities. Such information can have strong identifying power. The direct misclassification approach fully exploits it to derive identification regions for any real functional of the distribution of interest. A method for estimating the identification regions and construct their confidence sets is given, and illustrated with an empirical analysis of the distribution of pension plan types using data from the Health and Retirement Study.     

Data,Linkages and Models: US National Income and Product Accounts in the Framework of a Social Accounting Matrix

In policy analysis, there is a continuing tension and interplay between issues, models and data. Issues and models have changed in recent years, and there is a need for evolution in the underlying economy-wide economic data base. We discuss accounting frameworks for integrating micro-survey data with macro-data from the national economic accounts. We take a modeler's perspective, arguing that new accounts are needed to support policy modeling. We discuss in detail the use of a social accounting matrix (SAM). A SAM provides a data framework which reflects an actor/transaction view of the economy and supports disaggregated economy-wide modeling. We discuss the relationship between a SAM and the existing national economic accounts for the USA, including the national income and product accounts and the input–output accounts.