Robust inference in single firm/single event analyses

Single firm/single event (SFSE) studies are relevant in corporate finance. Since inference on abnormal returns in this context necessarily relies on the time series variance of these abnormal returns, the implied problem of heteroscedasticity is obvious, although hard to solve. We analyze robust inference in an SFSE setting using Monte Carlo and resampling experiments. Estimation is biased when the calibration and event period occur in different volatility regimes. We develop a unique specification test for these structural breaks. The most robust inference is obtained by using intraday data and a multiplicative component GARCH estimator.     

随机化检验在药物经济学评价中的应用

目前,国内关于药物经济学评价大多采用点估计的方法简单对比不同用药方案的评价指标,但是基于统计推断的理论,这种简单处理并不能反映研究人群总体的比较结果。本文将随机化检验应用于药物经济学评价中,能在一定程度上解决药物经济学疗效评价的问题。     

金融网络中个人经济行为资金流动仿真研究

在歪融网络中,资金以电子的速度在各个资金帐户节点之间流动,而当前在金融监管领域缺乏一种先进的资金流动检测技术,并且真实的交易数据又很难得到。本文提出了为了监测出正常及异常的资金流动模式研究基于Agent技术的个人账户资金流动仿真是必不可少的,本文界定了个人的经济行为包括消费行为和投资行为引起的资金流动,并且对于推理机的整体框架作出了详细设计。     

Distribution-free Approximate Methods for Constructing Confidence Intervals for Quantiles

Quantile estimation is important for a wide range of applications. While point estimates based on one or two order statistics are common, constructing confidence intervals around them, however, is a more difficult problem. This paper has two goals. First, it surveys the numerous distribution-free methods for constructing approximate confidence intervals for quantiles. These techniques can be divided roughly into four categories: using a pivotal quantity, resampling, interpolation, and empirical likelihood methods. Second, a method based on the pivotal quantity that has received limited attention in the past is extended. Comprehensive simulation studies are used to compare performance across methods. The proposed method is simple and performs similarly to linear interpolation methods and a smoothed empirical likelihood method. While the proposed method has slightly wider interval widths, it can be calculated for more extreme quantiles even when there are few observations.     

Two-Tier Store Brands: The Benefic Impact of a Value Brand on Perceptions of a Premium Brand

A recent trend in the retail industry is the emergence of multi-tier store brands. In the current article, we contrast consumers’ expectations in a retailing context in which a single store brand is present to one in which two store brands are offered in the same category. We propose that when a retailer offers a single store brand, consumers expect it to be of lower quality even when it is described as a premium brand. On the other hand, quality perception of a premium store brand increases in the presence of a value store brand. Importantly, quality perceptions of a value store brand are not affected by the presence of a premium store brand. In two studies we find support for our hypotheses showing the benefic impact of a value store brand on quality perceptions of a premium store brand.     

A new microstructure noise index

We introduce a new microstructure noise index for financial data. This index, the computation of which is based on the p-variations of the considered asset or rate at different time scales, can be interpreted in terms of Besov smoothness spaces. We study the behavior of our new index using empirical data. It gives rise to phenomena that a classical signature plot is unable to detect. In particular, with our data set, it enables us to separate the sampling frequencies into three zones: no microstructure noise for low frequencies, increasing microstructure noise from low to high frequencies, and some kind of additional regularity on the finest scales. We then investigate the index from a theoretical point of view, under various contexts of microstructure noise, trying to reproduce the facts observed on the data. We show that this can be partially done using models involving additive correlated errors or rounding error. Accurate reproduction seems to require either both kinds of error together or some unusual form of rounding error.     

Choosing options for products: the effects of mixed bundling on consumers’ inferences and choices

For product categories such as cars, computers, vacation packages, and new homes, consumers usually choose not only the product itself, but also various options for the product. Sellers decide how to present these options to consumers, and they often sell options both individually and in bundles (mixed bundling). In this research, we examine how mixed bundling affects consumers’ inferences about the options and choices among the options. We demonstrate that as long as the seller’s motives for bundling options are not perceived to be negative by consumers, options offered both individually and in bundles are perceived to be more important and are more likely to be chosen than options offered only individually.     

Evaluative and factual ad claims,knowledge level,and making inferences

A lab study examined the effect of type of ad claim (factual versus evaluative) and knowledge level on subjects' product feature inference making from three camera ads. Each ad had some missing brand information. The results showed that as knowledge increased, so did inference making. Also, there was a significant interaction between ad claim and knowledge. Inference making was positively correlated with knowledge level for factual ads but not for evaluative ones. Finally, subjects made very few inferences to fill in missing ad features but instead did more interpretive processing.