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.     

Assessing the Behaviour of Non-Survey Methods for Constructing Regional Input–Output Tables through a Monte Carlo Simulation

The paper aims to analyse the behaviour of a battery of non-survey techniques of constructing regional I-O tables in estimating impact. For this aim, a Monte Carlo simulation, based on the generation of ‘true’ multiregional I-O tables, was carried out. By aggregating multi-regional I-O tables, national I-O tables were obtained. From the latter, indirect regional tables were derived through the application of various regionalisation methods and the relevant multipliers were compared with the ‘true’ multipliers using a set of statistics. Three aspects of the behaviour of the methods have been analysed: performances to reproduce ‘true’ multipliers, variability of simulation error and direction of bias. The results have demonstrated that the Flegg et al. Location Quotient (FLQ) and its augmented version (AFLQ) represent an effective improvement of conventional techniques based on the use of location quotients in both reproducing ‘true’ multipliers and generating more stable simulation errors. In addition, the results have confirmed the existence of a tendency of the methods to over/underestimate impact. In the cases of the FLQ and the AFLQ, this tendency depends on the value of the parameter δ.     

Estimation in generalized bivariate Birnbaum–Saunders models

In this paper, we propose two moment-type estimation methods for the parameters of the generalized bivariate Birnbaum–Saunders distribution by taking advantage of some properties of the distribution. The proposed moment-type estimators are easy to compute and always exist uniquely. We derive the asymptotic distributions of these estimators and carry out a simulation study to evaluate the performance of all these estimators. The probability coverages of confidence intervals are also discussed. Finally, two examples are used to illustrate the proposed methods.     

An excellent approximation for the m out of n day provision

The m out of n day provision (MooN) of convertible bonds is difficult to handle. To approximating the MooN better, this paper proposes an approach named the conditional range probability (CRP). CRP is the simulated probability of the MooN being reached within a price range at a future time, conditional on today’s price of the underlying, and can be incorporated into any conventional derivatives pricing method. For a purposely designed exotic call option with a 20 out of 30 day provision, CRP under finite difference is found to outperform significantly several existing approaches and produce a mean pricing error of 1% over a wide range of initial underlying prices for the exotic call. The result implies that finite difference utilizing CRP will yield excellent approximating prices for convertible bonds.     

建设项目风险分析与蒙特卡罗仿真

结合建设项目特征,对建设项目全过程的风险分析方法与应用进行了深入研究。重点论述了蒙特卡罗模拟技术在建设项目风险分析中的应用。通过算例,进一步论证了该方法作为一种风险分析技术,具有独特优点和广泛适用性。     

基于灰色预测模型与季节指数的区域物流需求预测

针对灰色预测模型在区域物流中不能有效解决因季节变动而引起的物流需求变化的问题,引入季节指数的概念,构建基于灰色模型和季节指数的物流需求预测模型,并给出了相应的具体实施方案。最后以哈尔滨市物流需求统计数据为例,对所提方法进行了仿真分析,仿真结果表明了该方法的有效性和可行性。     

Choosing the Best Volatility Models: The Model Confidence Set Approach*

This paper applies the model confidence set (MCS) procedure of Hansen, Lunde and Nason (2003) to a set of volatility models. An MCS is analogous to the confidence interval of a parameter in the sense that it contains the best forecasting model with a certain probability. The key to the MCS is that it acknowledges the limitations of the information in the data. The empirical exercise is based on 55 volatility models and the MCS includes about a third of these when evaluated by mean square error, whereas the MCS contains only a VGARCH model when mean absolute deviation criterion is used. We conduct a simulation study which shows that the MCS captures the superior models across a range of significance levels. When we benchmark the MCS relative to a Bonferroni bound, the latter delivers inferior performance.     

LEC风险分析方法改进及在隧道施工中的应用研究

LEC方法是目前常用的风险分析方法,但是该方法风险发生概率的取定过于主观。针对这一缺陷,本文展开研究并进行改进,将原方法中的"风险发生可能性"主观打分确定改进为由"系统固有能量"和"人为的控制能量"两个因素确定。最后,本文将改进的模型应用于某公路项目的隧道施工安全风险管理中并取得了很好的效果。     

A new copula for modeling portfolios with skewed,leptokurtic and high-order dependent risk factors

The paper proposes a new copula for modeling higher-order dependencies between pairs of portfolio assets, employing orthogonal polynomials to model symmetric co-kurtoses. Skewness and leptokurtosis of portfolio margins are modeled either with the Gram–Charlier expansion of the Normal distribution or Gram–Charlier-like expansions of leptokurtic laws. Details on the estimation method of this copula are provided, and a simulation study is carried out to assess its potential range of applicability with respect to widely employed alternatives in the copula literature. Empirical evidence of the suitability of this approach to model financial data and compute risk measures is provided.     

Nonparametric checks for varying coefficient models with missing response at random

In this paper, we propose a test on the parametric form of the coefficient functions in the varying coefficient model with missing response. Two groups of completed data sets are constructed by using imputation and inverse probability weighting methods respectively. By noting that the coefficient part can be a regression function for a specific model, we construct two empirical process-based tests. The asymptotical distributions of the proposed tests under null and local alternative hypothesis are investigated respectively. Simulation study is carried out to show the power performance of the test. We illustrate the proposed approaches with an environmental data set.     

线性调频、非线性调频及相位编码信号脉冲压缩处理研究

本文首先对脉冲压缩处理中线性调频、非线性调频和相位编码三种发射信号进行深入的研究分析,然后对各种信号优缺点进行了分析对比,最后完成了线性调频信号脉冲压缩处理的matlab仿真,并对经过脉冲压缩处理后的不同的回波信号的旁瓣抑制比和处理增益进行分析计算,对今后的工作有一定的指导意义。     

UNCERTAINTY ANALYSIS FOR MULTI-REGION INPUT–OUTPUT MODELS – A CASE STUDY OF THE UK'S CARBON FOOTPRINT

This paper reviews and demonstrates methods available for estimating standard deviations for carbon multipliers in a multi-regional input–output (MRIO) framework. We attempt to capture all possible variations of underlying data and calculation procedures in a global MRIO model constructed with particular focus on the UK. We consider these variations to be random, and determine the stochastic variation of the whole MRIO system using Monte Carlo techniques. 5000 simulation runs were carried out to determine the standard deviations of multipliers. From these, the standard deviations of components of the UK's carbon footprint were estimated using error propagation. We estimate an 89% probability that the UK's carbon footprint has increased between 1994 and 2004.     

Ex-post evaluation of scenarios: the case of nanotechnology societal impacts

Scenario development is one of the popular methods of futures studies. There has been a lot of attention paid to different methods of scenario writing in theoretical literature; but what is important is to evaluate the scenarios which has not been a matter of comprehensive study heretofore. In this paper, we have carried out a comprehensive study in this regard. Accordingly, evaluation has been divided into three categories: ex-ante evaluation, mid-term evaluation and ex-post evaluation. Ex-ante evaluation is carried out when the scenario is generating; mid-term evaluation is carried out when the scenarios are transferring; and ex-post evaluation is being done after the transfer of scenarios. It should be noted that main purpose for ex-ante evaluation is considered “effectiveness”, for mid-term evaluation “relevance”, and for ex-post evaluation “the impact”. The outstanding part of this paper is the ex-post evaluation of scenarios for societal impact of nanotechnology, (which is carried out in accordance with a former paper) whose corresponding National Iranian Nanotechnology Initiative (NINI) time began approximately 10 years ago.     

A two–stage approach to additive time series models

For nonlinear additive time series models, an appealing approach used in the literature to estimate the nonparametric additive components is the projection method. In this paper, it is demonstrated that the projection method might not be efficient in an asymptotic sense. To estimate additive components efficiently, a two–stage approach is proposed together with a local linear fitting and a new bandwidth selector based on the nonparametric version of the Akaike information criterion. It is shown that the two–stage method not only achieves efficiency but also makes bandwidth selection relatively easier. Also, the asymptotic normality of the resulting estimator is established. A small simulation study is carried out to illustrate the proposed methodology and the two–stage approach is applied to a real example from econometrics.     

随机仓库布局问题模型与算法研究

通过讨论随机条件下仓库布局问题．建立了随机仓库布局问题机会约束规划模型，并设计出基于随机模拟的禁忌搜索算法求解模型，最后利用算例来验证算法的有效性。     

基于DSP_FPGA的车辆道路图像的处理平台设计

对于智能汽车而言,车辆道路图像的显示要求实时性高,处理器要处理的数据量非常大,因此我们以DSP_FPGA双模块实现高性能实时车辆道路图像处理。这种系统外围电路简单,功能强大,可以实现图像的识别、跟踪和匹配。本文对车辆道路图像处理平台的设计方法和硬件构成进行了详细的论述,并进行了仿真实验验证。     

Testing the adequacy of varying coefficient models with missing responses at random

In this paper, we investigate checking the adequacy of varying coefficient models with response missing at random. In doing so, we first construct two completed data sets based on imputation and marginal inverse probability weighted methods, respectively. The empirical process-based tests by using these two completed data sets are suggested and the asymptotic properties of the test statistics under the null and local alternative hypotheses are studied. Because the limiting null distribution is intractable, a Monte Carlo approach is applied to approximate the distribution to determine critical values. Simulation studies are carried out to examine the performance of our method, and a real data set from an environmental study is analyzed for illustration.     

Testing the compounding structure of the CP-INARCH model

A statistical test to distinguish between a Poisson INARCH model and a Compound Poisson INARCH model is proposed, based on the form of the probability generating function of the compounding distribution of the conditional law of the model. For first-order autoregression, the normality of the test statistics’ asymptotic distribution is established, either in the case where the model parameters are specified, or when such parameters are consistently estimated. As the test statistics’ law involves the moments of inverse conditional means of the Compound Poisson INARCH process, the analysis of their existence and calculation is performed by two approaches. For higher-order autoregressions, we use a bootstrap implementation of the test. A simulation study illustrating the finite-sample performance of this test methodology in what concerns its size and power concludes the paper.     

The multi-state latent factor intensity model for credit rating transitions

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions in a 7 grade rating system. The model includes a common dynamic component that can be interpreted as the credit cycle. Asymmetric effects of this cycle across rating grades and additional semi-Markov dynamics are found to be statistically significant. Finally, we investigate whether the common factor model suffices to capture systematic risk in rating transition data by introducing multiple factors in the model.     

Buyer perceptions of supply disruption risk: A behavioral view and empirical assessment

As supply chains become more complex, firms face increasing risks of supply disruptions. The process through which buyers make decisions in the face of these risks, however, has not been explored. Despite research highlighting the importance of behavioral approaches to risk, there is limited research that applies these views of risk in the supply chain literature. This paper addresses this gap by drawing on behavioral risk theory to investigate the causal relationships amongst situation, representations of risk, and decision-making within the purchasing domain. We operationalize and explore the relationship between three representations of supply disruption risk: magnitude of supply disruption, probability of supply disruption, and overall supply disruption risk. Additionally, we draw on exchange theories to identify product and market factors that impact buyers’ perceptions of the probability and magnitude of supply disruption. Finally, we look at how representations of risk affect the decision to seek alternative sources of supply. We test our model using data collected from 223 purchasing managers and buyers of direct materials. Our results show that both the probability and the magnitude of supply disruption are important to buyers’ overall perceptions of supply disruption risk. We also find that product and market situational factors impact perceptions of risk, but they are best understood through their impact on perceptions of probability and magnitude. Finally, we find that decisions are based on assessments of overall risk. These findings provide insight into the decision-making process and show that all three representations of risk are necessary for fully understanding risky decision-making with respect to supply disruptions.