VMI对医药供应链中零售商和批发商的利益影响研究

随着政府对医药卫生行业监管的加强,医药的流通环节已成为医药行业变革的重点之一。通过对行业产品及供应链特性的分析,本文提出医药行业实施供应商库存管理(VMI)是迎接变革的一个有效途径,然后定性定量地研究VMI如何增加医药零售商、批发商和整个供应链的利润。本文也给出VMI协调医药供应链的条件,以及批发商如何通过自身的管理提升来实现自我激励和促成同零售商的共赢。     

Stochastic model specification search for Gaussian and partial non-Gaussian state space models

Model specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time-varying. To this aim a new model space MCMC method is developed in this paper. It is based on extending the Bayesian variable selection approach which is usually applied to variable selection in regression models to state space models. For non-Gaussian state space models stochastic model search MCMC makes use of auxiliary mixture sampling. We focus on structural time series models including seasonal components, trend or intervention. The method is applied to various well-known time series.     

Estimation methods for stochastic volatility models: a survey

Abstract.  Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been introduced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.     

Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model, which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for hierarchical and for Minnesota-type priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.     

基于Markov链的物流园区需求灰色预测模型改进

物流因区是一项大型基础设施投资项目，在规划与设计上需要有一定的超前意识，而需求预测则是园区规划超前意识的重要依据。目前。国内外对物流园区需求预测方法的研究和实践基本上还处于发展阶段．至今尚未形成完整科学、操作性强的方法体系，现有的需求预测大体上是借鉴史通运输规划的模型．进行研究的过程中灰色模型预测有局限性。其预测结果与真实值之间存在一定误差，因而可以考虑利用Markov链提出改进方案。     

提升南通产业体系竞争优势研究 ——基于融入全球价值链重构的视角

基于融入全球价值链重构的视角,秉承"外引内育、承上启下"的思想,研究南通提升产业体系竞争优势的问题,多角度分析南通产业体系竞争优势,研究表明处于制造大省,南通的制造业在全国范围内跻身前列,与省内各城市相比,南通的第三产业更具优势。虽然南通各产业实现了量上的不断突破,然而徘徊于价值链中低端的现状抑制了高质量发展,且低成本优势不复存在。南通应当抓住全球价值链重构的机遇,优化产业布局、加快高新产业发展,同时整合有效资源、完善人才建设机制从而提升产业体系竞争优势。     

Goodwill and dynamic advertising strategies

In this paper, we empirically analyze weekly advertising policies of manufacturing firms in consumer goods markets. We assume firms engage in persuasive advertising, thus policies of firms affect the goodwill of a brand. We introduce a demand and a goodwill production function. A simple transformation of the demand function allows us to identify not only the demand parameters but also the parameters of the goodwill production function. We reconstruct the unobserved goodwill levels using these parameters and past advertising levels. We restrict our attention to Markov Perfect Equilibrium (MPE) strategies which are functions of payoff relevant state variables. Without imposing further restrictions on the dynamic competitive environment, we investigate the relationship between observed advertising strategies—which are assumed to be MPE—and payoff relevant state variables by means of several reduced form specifications. The most important determinant of advertising intensity turns out to be goodwill. We demonstrate that controlling for an advertising campaign significantly improves the explanatory power of the model. JEL Classification L13 . C73 . M30 . M37 I would like to thank Volkswagen Stiftung for the generous financial support which made this research possible.