Evaluating inflation persistence considering model uncertainty and structural break

There is little consensus about the historical change in inflation persistence. One of the reasons is that researchers cannot choose a uniform model structure due to model uncertainty. In this study, a model to investigate inflation persistence is proposed. Unlike previous studies, I try to overcome model uncertainty by being agnostic about the structure of the model. Estimation results indicate that inflation has become less persistent over time, implying that inflation persistence is related to the way how monetary policy is conducted.     

食品抽样检测的方法及技巧研究

三聚氰胺,苏丹红以及地沟油等事件,给人们带来了极大的食品安全隐患,影响了人们身体健康,严重的甚至导致死亡,因此人们对于食品安全越来越重视,对食品安全检测的信任度越来越低。     

Formulating hypothetical scenarios in correlation stress testing via a Bayesian framework

Correlation stress testing refers to the correlation matrix adjustment to evaluate potential impact of the changes in correlations under financial crises. There are two categories, sensitivity tests and scenario tests. For a scenario test, the correlation matrix is adjusted to mimic the situation under an underlying stress event. It is only natural that when some correlations are altered, the other correlations (peripheral correlations) should vary as well. However, most existing methods ignore this potential change in peripheral correlations. In this paper, we propose a Bayesian correlation adjustment method to give a new correlation matrix for a scenario test based on the original correlation matrix and views on correlations such that peripheral correlations are altered according to the dependence structure of empirical correlations. The algorithm of posterior simulation is also extended so that two correlations can be updated in one Gibbs sampler step. This greatly enhances the rate of convergence. The proposed method is applied to an international stock portfolio dataset.     

A Monte Carlo Study on the Finite Sample Properties of the Gibbs Sampling Method for a Stochastic Frontier Model

In this paper we use Monte Carlo study to investigate the finite sample properties of the Bayesian estimator obtained by the Gibbs sampler and its classical counterpart (i.e. the MLE) for a stochastic frontier model. Our Monte Carlo results show that the MSE performance of the estimates of Gibbs sampling are substantially better than that of the MLE.     

Choosing a digital content strategy: How much should be free?

Advertising supported content sampling is ubiquitous in online markets for digital information goods. Yet, little is known about the profit impact of sampling when it serves the dual purpose of disclosing content quality and generating advertising revenue. This paper proposes an analytical framework to study the optimal content strategy for online publishers and shows how it is determined by characteristics of both the content market and the advertising market. The strategy choice is among a paid content strategy, a sampling strategy, and a free content strategy, which follow from the publisher's decisions concerning the size of the sample and the price of the paid content. We show that a key driver of the strategy choice is how sampling affects the prior expectations of consumers, who learn about content quality from the inspection of the free samples. Surprisingly, we find that it can be optimal for the publisher to generate advertising revenue by offering free samples even when sampling reduces both prior quality expectations and content demand. In addition, we show that it can be optimal for the publisher to refrain from revealing quality through free samples when advertising effectiveness is low and content quality is high. To illustrate, we relate our framework to the newspaper industry, where the sampling strategy is known as the “metered model.”     

A multistage stochastic programming model for the New Zealand dairy industry

This paper presents an application of multistage stochastic programming to a production planning problem for Fonterra, a leading company in the New Zealand dairy industry, taking into account uncertain milk supply, price–demand curves and contracting. We describe a model for Fonterra's supply chain, and a model for uncertain milk supply. We then present a multistage stochastic quadratic programming model and a decomposition algorithm to compute an optimal sales policy, which is tested in simulation against a deterministic policy.     

样本效应与软件产品定价策略研究

样本试用之所以成为软件产品重要的销售策略,目的在于降低消费者对产品功能水平认知的不确定性,奠定市场基础.基于收益管理理论,结合学习效应和网络外部性,分析消费者行为和市场期望均衡,本文建立了目标最大化的收益模型,探讨了最优的定价策略,通过数值分析相关参数对最优价格、销售量和收益的影响.结论显示:在试用期限的限制下,将最优的定价策略划分为三个区域,包括:(1)无盗版、无威胁;(2)无盗版、有威胁;(3)有盗版;在盗版的风险成本较高情况下,较低的定价和较短的试用期限将发挥优势,并对价格、试用期限、销售量和盗版的风险成本之间地权衡抉择可取得最大收益.     

Existence of balanced sampling plans avoiding cyclic distances

This article considers balanced sampling plans avoiding adjacent units. It is shown, using linear algebraic and design theoretic tools, that such plans exist for all sufficiently large populations. More generally, if any set of m circular distances is to be avoided in balanced sampling with subsets of size k, then such a plan exists, provided the population size N is at least mk ³. Research of the Peter J. Dukes is supported by NSERC.     

Are there waves in merger activity after all?

This paper investigates the merger wave hypothesis for the US and the UK employing a Markov regime-switching model. Using quarterly data covering the last 30 years, for the US, we identify the beginning of a merger wave in the mid 1990s but not the much-discussed 1980s merger wave. We argue that the latter finding can be ascribed to the refined methods of inference offered by the Gibbs sampling approach. As opposed to the US, mergers in the UK exhibit multiple waves, with activity surging in the early 1970s and the late 1980s.     

频标计量仪器电路的低噪声设计

本文论述了频标计量仪器中倍频器和分频器两种电路的设计思想。通过对经典电路的分析,简要介绍了降低电路噪声的工艺。实践证明,这些工艺是很有效的。