均衡选址方法研究

本文首次利用聚类分析将给定地区的消费者的空间分布表示成一个加权层次聚类图,为选址研究开辟了一条新途径.在此基础上,通过分析垄断企业和消费者的行为,提出了如何求解垄断企业提供的均衡的多个设施所在的区域及设施数目的新方法.文章认为,均衡的设施数目不仅受企业要保证的设施覆盖率的影响,而且也受消费者愿意行走的最大距离的限制.当设施覆盖率不变时,随着消费者愿意行走的最大距离的增加,设施数目减少,覆盖率不会减少,常会增加;当消费者愿意行走的最大距离不变时,随着设施覆盖率的降低,设施数目不会减少,常会增加.覆盖率和最大距离的增加,有助于减少均衡的设施数目.     

江苏省国土资源数字化管理浅议

数字国土资源是以计算机、多媒体和大规模存储等技术为基础,以宽带网络为纽带运用大量国土资源信息对区域国土资源进行三维描述并用于改善人们的经济活动和生活质量。江苏省数字国土资源目标应本着循序渐进不断完善的原则,着眼于省内基础地质、矿产资源、土地资源等基础数据,开发国土资源信息软件,拓宽服务领域。     

Implicit expectiles and measures of implied volatility

It is well known that the normal distribution is inadequate in capturing the skewed and heavy-tailed behaviour of exchange rate returns. To this end, various flexible distributions that are capable of modelling the asymmetric and tailed behaviour of returns have been proposed. In this paper, we investigate the performance of the generalized lambda distribution (GLD) to capture the skewed and leptokurtic behaviour of exchange rate returns. We do this by conducting a comprehensive numerical study to compare the performance of the GLD against the performances of the skewed t distribution, the unbounded Johnson family of distributions and the normal inverse Gaussian (NIG) distribution. Our results suggest that in terms of the value-at-risk and expected shortfall, the GLD shows at least similar performance to the skewed t distribution and the NIG distribution. Considering the ease in GLD’s use for random variate generation in Monte Carlo simulations, we conclude that the GLD can be a good alternative in various financial applications where modelling of the heavy tail behaviour is critical.     

Estimation of P[Y < X] for generalized exponential distribution

This paper deals with the estimation of P[Y < X] when X and Y are two independent generalized exponential distributions with different shape parameters but having the same scale parameters. The maximum likelihood estimator and its asymptotic distribution is obtained. The asymptotic distribution is used to construct an asymptotic confidence interval of P[Y < X]. Assuming that the common scale parameter is known, the maximum likelihood estimator, uniformly minimum variance unbiased estimator and Bayes estimator of P[Y < X] are obtained. Different confidence intervals are proposed. Monte Carlo simulations are performed to compare the different proposed methods. Analysis of a simulated data set has also been presented for illustrative purposes.Part of the work was supported by a grant from the Natural Sciences and Engineering Research Council     

On characterizations of distributions by regression of adjacent generalized order statistics

Let , r ≥ 1, denote generalized order statistics, with arbitrary parameters , based on distribution function F. In this paper we characterize continuous distributions F by the regression of adjacent generalized order statistics, i.e. where are continuous and increasing functions and ψ is strictly increasing. Further we investigate in detail the case when ψ(x) = x and g is a linear function of the form g(x) = cx + d for some .     

Industry R&D intensity distributions: regularities and underlying determinants

This paper aims to examine the nature of the distributions of firm R&D intensities within industries and explore the factors that underlie the industry R&D intensity distributions. In particular, following the seminal study by Cohen and Klepper (1992) and using some new and rich data on firm R&D intensities for seven industries across six countries, this study examines the regularities in the industry R&D intensity distributions and demonstrates, based on a simple model of firm R&D, that the industry R&D intensity distributions are governed by the distributions of technological competence, a measure of firm R&D productivity, which corresponds to the notion of the “unobserved R&D-related capabilities” suggested by Cohen and Klepper (1992). This study found that firm R&D intensities within industries are lognormally distributed, displaying a strikingly regular pattern across industries, that the industry distributions of the levels of technological competence are also lognormal, and that, based on the formal model of firm R&D and the notion of the unobserved R&D-related capabilities, the distribution of firm technological competence within an industry underlies the industry's firm R&D intensity distribution.     

On the statistical differences between binary forecasts and real-world payoffs

We map the difference between (univariate) binary predictions, bets and “beliefs” (expressed as a specific “event” will happen/will not happen) and real-world continuous payoffs (numerical benefits/harm from an event) and show the effect of their conflation and mischaracterization in the decision-science literature. We also examine the differences under thin and fat tails. The effects: [A] Spuriousness of many psychological results, particularly those documenting that humans overestimate tail probabilities. We quantify such conflations. [B] Being a “good forecaster” in binary space doesn’t lead to having a good actual performance, and vice versa, especially under nonlinearities. A binary forecasting record is likely to be a reverse indicator under some classes of distributions or deeper uncertainty. [C] Machine Learning: Some nonlinear payoff functions, while not lending themselves to verbalistic expressions, are well captured by ML or expressed in option contracts. Fattailedness: The difference is exacerbated in the power law classes of probability distributions.     

Do we need higher-order comoments to enhance mean-variance portfolios? Evidence from a simplified jump process

We consider a joint distribution that decomposes asset returns into two independent components: an elliptical innovation (Gaussian) and a systematic non-elliptical latent process. The paper provides a tractable approach to estimate the underlying parameters and, hence, the assets’ exposures to the latent non-elliptical factor. Additionally, the framework incorporates higher-order moments, such as skewness and kurtosis, for portfolio selection. Taking into account estimation risk, we investigate the economic contribution of the non-elliptical term. Overall, we find weak empirical evidence to support the inclusion of the non-elliptical term and, hence, the higher-order comoments. Nonetheless, our findings support the mean–variance (MV) decision rule that incorporates the elliptical term alone. Excluding the non-elliptical term results in more robust mean–variance estimates and, thus, enhanced out-of-sample performance. This evidence is significant among stocks that exhibit a strong deviation from the Gaussian property. Moreover, it is most pronounced during market turmoils, when exposures to the latent factor are highest.     

Uncertainty and Amenity Values in Renewable Resource Economics

In this paper, we study the effects of amenities and uncertainty on the optimal exploitation of a renewable resource. First, if society displays a protection motive (in the presence of amenities), its optimal harvesting strategy will be more conservative. Second, we show that ecological uncertainty leads society to develop the opposite behavior, thus reducing its exposition towards risk. A stationary analysis is undertaken to analytically quantify and compare these two effects. With a Gompertz natural regeneration function and a proportional risk, the amenity effect is proved to prevail if and only if stochastic fluctuations on the resource stock are not too large.     

Characterizations of logarithmic series distrimtrans

The distributions of X, Y and (X. Y ), where X and Y are random variables with probability functions of a logarithmic series law, are characterized by the regression function of X on Y and the conditional distribution of Y given X. Moreover, characterizations are given for binomial or Pascal conditional distributions in terms of the regression function of X on Y and the marginal distribution of X.