Demand Systems Estimation With Microdata: A Censored Regression Approach

Demand systems estimation increasingly makes use of household-level microdata, mainly to measure the effects of demographic variables. Data based on these household-expenditure surveys present a major estimation problem. For any given household, many of the goods have zero consumption, implying a censored dependent variable. Techniques which do not take this censored dependent variable into account will yield biased results. We utilize a censored regression approach that is computationally simple, consistent, and asymptotically efficient. The results are then presented and compared with those obtained using an uncensored technique.     

Taking into account the rescheduling problem during the scheduling phase

The scheduling problem in production management has been studied for a considerable time, and several types of software are used. A problem arises in updating the production planning, or ‘rescheduling’, when an unexpected event occurs in the shop control. Solving this problem is difficult because the implications of such events are usually impossible to forecast. To prevent this problem, we propose to manipulate a set of equivalent schedules during the short time schedule. Then, if an unexpected event prevents realization of a given schedule, it will be possible to find an equivalent one, without full rescheduling. The primary requirement is to find a formal representation of a set of schedules. This has already been explored using CPM graphs with nodes associated to a set of tasks. We propose in this paper to use an extension of such graphs, PQR trees, that represent both precedence and group constraints. We first reiterate the notion of PQR trees. We present methods to take into account date constraints in such a structure, and we give a model for the general job-shop problem.     

Case–control genome-wide joint association study using semiparametric empirical model and approximate Bayes factor

We propose a semiparametric approach for the analysis of case–control genome-wide association study. Parametric components are used to model both the conditional distribution of the case status given the covariates and the distribution of genotype counts, whereas the distribution of the covariates are modelled nonparametrically. This yields a direct and joint modelling of the case status, covariates and genotype counts, and gives a better understanding of the disease mechanism and results in more reliable conclusions. Side information, such as the disease prevalence, can be conveniently incorporated into the model by an empirical likelihood approach and leads to more efficient estimates and a powerful test in the detection of disease-associated SNPs. Profiling is used to eliminate a nuisance nonparametric component, and the resulting profile empirical likelihood estimates are shown to be consistent and asymptotically normal. For the hypothesis test on disease association, we apply the approximate Bayes factor (ABF) which is computationally simple and most desirable in genome-wide association studies where hundreds of thousands to a million genetic markers are tested. We treat the approximate Bayes factor as a hybrid Bayes factor which replaces the full data by the maximum likelihood estimates of the parameters of interest in the full model and derive it under a general setting. The deviation from Hardy–Weinberg Equilibrium (HWE) is also taken into account and the ABF for HWE using cases is shown to provide evidence of association between a disease and a genetic marker. Simulation studies and an application are further provided to illustrate the utility of the proposed methodology.     

Book Reviews

This article examines some difficulties associated with the Haynes–Stone model and attempts to clarify how the model can be justified as representing the hypothesis that “quantity is demand determined and price is supply determined.” It also argues that the applications of such a model as was done by Haynes and Stone might not have resolved the controversies related to the Phillips curve and the supply function of exports (or imports).     

The True Cost-of-Living Index With Changing Preferences

This article investigates the theoretical and empirical properties of a true cost-of-living index under conditions of changing preferences. A family of true indexes is defined based on the notion of the current utility function. A particular index, based on the previous period's utility level, is then defined. Given this definition, a true cost-of-living index is computed based on a quadratic expenditure system estimated with quarterly data from 1960–1981. For empirical purposes, changes in preferences are represented by the linear habit formation hypothesis. This index is then compared with Paasche and Laspeyres indexes. The true cost-of-living index grows somewhat faster than either the Paasche or Laspeyres index. It also displays considerably more variability.     

保障房市场与商品房市场关系研究

保障房和商品房在本质上是差异化产品,两者在对应的需求层次、房屋的品质、价格、供给和渠道等方面都存在着巨大的差别。自我国房地产市场化以来,商品房就始终处于市场独大的局面,但随着近年来我国政府加大了对楼市的调控、加快了保障房建设的步伐,房地产市场已朝着保障房市场和商品房市场并重的＂双轨制＂迈进。然而,保障房的大规模兴建和投放市场会对商品房市场乃至整个中国楼市产生什么影响,以及保障房供给体系在运行与管理过程中存在什么问题,目前尚不确定。为此,有必要对其深入分析并寻找对策。     

农副产品营养质量模糊识别与评价

本文利用模糊集理论与决策科学，结合营养学知识，建立了农副产品营养质量识别与评价体系，并对1358种农副产品进行评判，效果较好。     

Newsvendor Problems with Demand Shocks and Unknown Demand Distributions

In today's competitive market, demand volume and even the underlying demand distribution can change quickly for a newsvendor seller. We refer to sudden changes in demand distribution as demand shocks. When a newsvendor seller has limited demand distribution information and also experiences underlying demand shocks, the majority of existing methods for newsvendor problems may not work well since they either require demand distribution information or assume stationary demand distribution. We present a new, robust, and effective machine learning algorithm for newsvendor problems with demand shocks but without any demand distribution information. The algorithm needs only an approximate estimate of the lower and upper bounds of demand range; no other knowledge such as demand mean, variance, or distribution type is necessary. We establish the theoretical bounds that determine this machine learning algorithm's performance in handling demand shocks. Computational experiments show that this algorithm outperforms the traditional approaches in a variety of situations including large and frequent shocks of the demand mean. The method can also be used as a meta‐algorithm by incorporating other traditional approaches as experts. Working together, the original algorithm and the extended meta‐algorithm can help manufacturers and retailers better adapt their production and inventory control decisions in dynamic environments where demand information is limited and demand shocks are frequent      

近代日本消费需求变动的因素分析

长期影响近代日本消费需求变动的因素主要有四个方面:国民经济的不断增长和国民收入的不断提高是近代日本消费需求逐渐上升的物质基础和前提条件;近代工矿交通事业的发展,使国民收入分配格局朝着有利于劳动的方向转变,这是促进近代日本消费需求不断上升的积极力量;人口规模及其构成的变动是近代日本消费需求不断上升的重要推动力量;消费的示范效应、攀比行为和不可逆行为对消费需求不断上升起着一定的推动作用。     

对"出口导向"和"进口替代"两种外贸发展战略的再认识

本文对进口替代和出口导向两种外贸战略进行了分析,认为我国应该充分利用大国的优势,立足于国内需求,降低对外依存度,同时应根据经济全球化的内在要求积极与世界经济接轨,实施进口替代与出口导向相结合的外贸战略,以成功应对经济全球化的挑战。