基于随机需求的物流配送中心选址离散模型研究

针对物流需求不确定情况下的物流配送中心选址问题,对传统模型进行改进,将随机需求变量引入离散型选址模型,利用随机规划理论和遗传算法对实例模型进行求解。结果显示物流需求不确定情况下的随机规划模型的求解结果比假设已知需求情况下的结果真实可信,所需物流费用较少。     

A comparison of regret-based and utility-based discrete choice modelling – an empirical illustration with hospital bed choice

There is some concern that the unobserved preference heterogeneity in random utility maximization theory-based discrete choice experiment modelling is an important source of error variability. The randomness in utility is often interpreted as interpersonal preference heterogeneity but it can also be intrapersonal random variation in preferences. We compare utility maximization and regret minimization-based choice models’ sensitivity to individual heterogeneity, examine differences between two consecrated models and validate with empirical illustrations. We use frequency of category (public, semi-private, and private) of bed chosen from Swiss cross-sectional datasets (2007–2012) to compare two approaches – utility maximization and regret minimization by applying multinomial logit (MNL) models in regard to the variances in utility (regret) function, goodness-of-fit and predicted marginal effects (pseudo-elasticity) of additional payment. We find parameters with the same sign and estimates with almost same order of magnitude in both the approaches. The statistical significance of attribute effects is consistent in all variants of utility -based MNL models while effects of different attributes are significant only in heteroskedastic extreme value (HEV) variant of regret-based MNL models. This empirical illustration suggests that HEV variant of regret-based models perform better in capturing attribute effects in choice behaviour.     

An alternative approach for portfolio performance evaluation: enabling fund evaluation relative to peer group via Malkiel’s monkey

We propose an implementable portfolio performance evaluation procedure that compares a portfolio with respect to the portfolios constructed by an infinite number of Malkiel’s blindfolded monkeys, or equivalently the whole enumeration of all possible portfolios. We argue that this approach exhibits two main advantages. First, it does not require any benchmark portfolios because a portfolio is being compared to an infinite number of portfolios. Second, it is market condition invariant. Since the market conditions are already reflected in the portfolio performances of an infinite blindfolded monkeys, our measure of portfolio performances is invariant to volatile market conditions.     

Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?

Structural breaks have been suggested by several economists as a possible explanation for the Meese–Rogoff puzzle, in the sense that an exchange rate model can outperform the random walk in terms of the out-of-sample forecasting error if the period under investigation is free of structural breaks. The results indicate that structural breaks cannot explain the inability of the flexible price monetary model to outperform the random walk. The only plausible explanation for the Meese–Rogoff puzzle is that forecasting accuracy is traditionally assessed by magnitude-only measures. When forecasting accuracy is assessed by alternative measures that do not rely exclusively on the magnitude of error, the monetary model can outperform the random walk regardless of the presence or otherwise of structural breaks.     

长沙夜市居民环境意识现状研究

夜市经济在给社会带来繁荣的同时,也造成了环境质量的下降。本研究采用问卷调查的方式,对长沙夜市集中地段的居民就居民的环境意识、居民对夜市与环境的认识、政府对夜市环境的管理力度等方面的情况进行了随机调查。结果表明：长沙夜市环境虽然在政府的努力下有所改善,但仍然存在许多问题。因此,应该划定和设立专门的夜市经营区域,实行准入制度,打击无照经营,加强对占道经营的专项整治,整顿夜市行业卫生,加强居民及经营者的环境保护宣传教育工作。     

Ruin probabilities in models with a Markov chain dependence structure

In this paper we derive explicit expressions for the probability of ruin in a renewal risk model with dependence among the increments (Z _k ) _k>0. We study the case where the dependence structure among (Z _k ) _k>0 is driven by a Markov chain with a transition kernel that can be described via ordinary differential equations with constant coefficients.     

A cost-benefit analysis of the random assignment UK Employment Retention and Advancement Demonstration

This article presents a cost-benefit analysis of Britain’s Employment Retention and Advancement (ERA) demonstration, which was evaluated through the first large-scale randomized control trial in the UK. ERA used a combination of job coaching and financial incentives in attempting to help long-term unemployed men and low-income lone parents sustain employment and progress in work once they were employed. Using both administrative and survey data, ERA’s effects on benefits and costs were estimated through impact analyses, which exploited the experimental design. The findings indicated that ERA was cost beneficial for long-term unemployed adult men, but not for lone parents. The key findings appear robust to sensitivity tests. Uncertainty, as implied by the SEs of the estimated impacts, was addressed through a Monte Carlo analysis, an approach seldom previously used in cost-benefit analyses of social programs.     

Is There Long Memory in Indian Stock Market Returns? An Empirical Search

The issue of long memory, though has important theoretical and practical implications, has not received much attention in India. This article examines the issue of long memory in mean of the stock returns by employing a set of sophisticated time-series tests including a bias reduced log periodogram test of Andrews and Guggenberger. The study used daily values of 29 major indices including sectoral indices traded on the National Stock Exchange and Bombay Stock Exchange from April 2003 to March 2012, which provide insights into relation between composition of indices and long memory. The findings of the study suggest significant presence of long memory in mean returns of the medium- and small-sized indices and weaker evidences for large cap indices. Further, the study identifies a relationship between presence of long memory and market structure variables. The use of linear models in the presence of long memory would result in incorrect inferences, and this calls for investigation of appropriate long memory model to generate profits in Indian stock market.