竞买者出价来到过程的数学模型及其应用研究

利用基于MatLab编写的计算程序，对从eBay网站收集的20个拍卖子类的竞买者出价数据进行了分析，发现网上拍卖中竞买者出价次数随拍卖进程呈现三阶段特征；竞买者出价速率具有时变性，可利用Shmueli等给出的三段速率函数表达式进行描述；三段出价速率函数的非齐次泊松过程很好地刻画了网上拍卖中竞买者出价来到过程的特征。     

基于函数性数据方法的艺术品网上拍卖竞买者出价水平影响因素研究

在分析互联网艺术品拍卖中竞买者出价水平特有影响因素的基础上,运用函数性相平面图与线性回归方法,探究出价水平及各影响因素在整个拍卖期间的动态变化情况。结果表明,在拍卖的不同阶段,推动竞买者出价水平变化的影响因素各有不同,且作用大小与作用方向在整个拍卖期间不断变化。特别指出,拍卖次序在整个拍卖期间对出价水平具有正向影响,且作用程度随着拍卖的进行逐渐增强,即序号越大、越晚参与竞拍的艺术品的最终成交金额越大。     

网上拍卖中竞买者出价数据的特征及分析方法研究

在传统统计分析中，研究者面对的数值型数据有三种形式，即横截面数据、时间序列数据以及混合数据。这些类型的数据具有离散、等间隔分布、密度均匀等特点，它们是传统的描述性统计和推断性统计中最主要的数据分析对象。然而，从拍卖网站收集到的诸如竞买者出价等数据，却不具备这些特点，对传统统计分析方法提出了挑战。因此需要从数据容量、数据的混合性、不等间隔分布及数据密度等方面，对网上拍卖数据的产生机制进行阐释，对其特征进行分析，并结合实际网上拍卖资料给出分析此类数据的方法和过程。     

中国文化产业与旅游产业融合发展的内在机理及时空分异

文章从“内在逻辑”和“外部动力”两个维度构建文化产业与旅游产业融合发展的内在机理模型,利用文化产业与旅游产业发展的面板数据,分析2009—2019年中国31个省份两大产业综合发展水平的时空变化特征,二者融合发展的耦合协调度的时空分异、动态演变特征及主要驱动因素。结果表明：两大产业综合发展水平在时间上呈现稳步增长态势,在空间上呈现“东高西低”的梯级分化分布格局;从时空演变来看,两大产业的耦合协调度在时间上呈现整体上升的态势且初步实现了高级耦合协调,在空间上呈现“东升西降、南高北低”的非均衡发展态势;两大产业的耦合协调类型具有较高的稳定性,短期内难以实现跨越式发展;第三产业劳动力及区域经济发展水平对两大产业间的耦合协调关系的影响最大。     

江苏省可持续发展系统调控试验模式及分析

一、江苏省可持续发展系统的自然演变模式　　江苏省可持续发展系统的自然演变模式是按照历史发展水平 ,假设不加人工干预地使系统顺延发展而设计的模式。本模型中的自然演变模式是按“八五”时期的指导思想、目标及布局顺延发展 ,不作大的调整进行的。江苏省人口、资源、环境与经济系统自然演变模式演变结果如表 1反映了自然演变模式下江苏人口、资源、环境与经济发展的动态变化情况。1 人口 :至 2 0 1 5年 ,江苏省人口将达 7838 31万人 ,较 1 995年增加 772 2 9万人 ,年均净增加 38万人。2 资源 :从土地资源看 ,耕地面积逐年减少 ,到 2…     

中国城镇居民消费支出结构研究

居民消费支出结构模式的形成与演变,一方面取决于一个国家现实的生产力发展水平,产业和产品的结构等因素,另一方面,它也对生产力发展、产业和产品结构的变化起着巨大的反作用。因此,了解、研究当前城镇居民消费支出结构模式及其演变规律,对于正确引导居民的消费行为,形成合理的消费支出结构,使其成为促进生产力发展的积极因素具有重要意义。本文试图从纵的方面——建国以来我国城镇居民消费支出结构演变的特点和成因,及横     

中国通货膨胀率的非线性动态行为研究——来自MS—ARFIMA模型的新证据

使用允许长记忆参数d服从区制转换的MS—ARFIMA模型对中国月度通货膨胀路径的动态行为进行新的实证研究,结果显示：中国通货膨胀不仅均值水平和不确定性存在着“低通胀”区制和“高通胀”区制,而且更为重要的是,通货膨胀序列的平稳性也表现出显著的区制转换动态。“低通胀”区制下,长记忆参数d1=0．361,说明通货膨胀是协方差平稳序列,“高通胀”区制下,长记忆参数d2=1．145,说明通货膨胀是非平稳序列。这一新的研究结论意味着中国通货膨胀冲击的持久性效应也存在相应的区制转移变化。这要求央行在管控通货膨胀过程中,既要考虑均值和不确定性的区制变化,又要兼顾平稳性和持久性的区制变化。     

发展电子商务势所必然

坐在办公室里,对着计算机,与远隔重洋的美国客商谈判、交易,已不再是梦想,2001年11月28日,中美首次进行了电子商务“无缝对接”,并在网上开展竞价和成交演示。演示会会场立着两块大屏幕,同时显示中美双方竞价和成交情况。美国日用品进口公司,发出求购纳米粉的信息,每公斤出价5000美元。中国纳米高科技公司在网上搜索后,找到这一信息,但是对价格不满意。于是,操作者在键盘上输入中文:价格太低了,至少要5500美元。几秒钟之后,美方的电脑自动翻译成英方。美方用英文输入5500美元超过我们的承受力,最多5300美元。中方纳米公司看到了用中文显示的…     

经济增长与产业、就业结构演变的实证研究——基于经济转型期陕西案例的研究

在经济转型期背景下,通过建立VECM模型来研究陕西经济增长与产业、就业结构之间的关系。实证结果表明：陕西产业结构演变带有明显转型期特征,经济增长对产业结构演变几乎不产生影响,在经济转型期,外生的产业结构与市场化改革的冲突是产业结构演变的内在原因;经济转型期产业、就业结构的演变对经济增长分别呈现“负效应”和“正效应”,但影响并不十分明显;从VECM模型的短期动态部分看,产业结构成为就业结构改善的显著障碍,产业结构的所有制性质及重型化特征,限制了劳动力转移对经济增长的作用。     

我国三次产业结构的历史演变及发展趋势

我国三次产业结构的演变及规律对我国产业结构历史演变轨迹进行统计分析,我们可从国内生产总值、从业人数两个角度进行考察:1.按GDP的角度进行统计分析。我们可从GDP（国内生产总值）的角度把我国三次产业结构的演变过程划分为五个阶段,见表一:从表一可见,我国从1952年以来按GDP计算的三次产业结构的演变过程经历了五个发展阶段,离结构类型最高形态“三二一”只差一个发展阶段,反映出我国产业结构变化虽有曲折,但总的来看,随着生产力的不断提高,产业结构处于不断优化升级的过程之中,取得了很大的成绩。2.按从业人数的角度进行…     

Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis

Online auctions have become increasingly popular in recent years, and as a consequence there is a growing body of empirical research on this topic. Most of that research treats data from online auctions as cross-sectional, and consequently ignores the changing dynamics that occur during an auction. In this article we take a different look at online auctions and propose to study an auction's price evolution and associated price dynamics. Specifically, we develop a dynamic forecasting system to predict the price of an ongoing auction. By dynamic, we mean that the model can predict the price of an auction “in progress” and can update its prediction based on newly arriving information. Forecasting price in online auctions is challenging because traditional forecasting methods cannot adequately account for two features of online auction data: (1) the unequal spacing of bids and (2) the changing dynamics of price and bidding throughout the auction. Our dynamic forecasting model accounts for these special features by using modern functional data analysis techniques. Specifically, we estimate an auction's price velocity and acceleration and use these dynamics, together with other auction-related information, to develop a dynamic functional forecasting model. We also use the functional context to systematically describe the empirical regularities of auction dynamics. We apply our method to a novel set of Harry Potter and Microsoft Xbox data and show that our forecasting model outperforms traditional methods.     

HOW WELL DO SELECTION MODELS PERFORM? ASSESSING THE ACURACY OF ART AUCTION PRE-SALE ESTIMATES

Art auction catalogs provide a pre-sale prediction interval for the price each item is expected to fetch. When the owner consigns art work to the auction house, a reserve price is agreed upon, which is not announced to the bidders. If the highest bid does not reach it, the item is brought in. Since only the prices of the sold items are published, analysts only have a biased sample to examine due to the selective sale process. Relying on the published data leads to underestimating the forecast error of the pre-sale estimates. However, we were able to obtain several art auction catalogs with the highest bids for the unsold items as well as those of the sold items. With these data we were able to evaluate the accuracy of the predictions of the sale prices or highest bids for all item obtained from the original Heckman selection model that assumed normal error distributions as well as those derived from an alternative model using the t(2) distribution, which yielded a noticeably better fit to several sets of auction data. The measures of prediction accuracy are of more than academic interest as they are used by auction participants to guide their bidding or selling strategy, and similar appraisals are accepted by the US Internal Revenue Services to justify the deductions for charitable contributions donors make on their tax returns.     

Random-Time Aggregation in Partial Adjustment Models

Structural econometric auction models with explicit game-theoretic modeling of bidding strategies have been quite a challenge from a methodological perspective, especially within the common value framework. We develop a Bayesian analysis of the hierarchical Gaussian common value model with stochastic entry introduced by Bajari and Hortaçsu. A key component of our approach is an accurate and easily interpretable analytical approximation of the equilibrium bid function, resulting in a fast and numerically stable evaluation of the likelihood function. We extend the analysis to situations with positive valuations using a hierarchical gamma model. We use a Bayesian variable selection algorithm that simultaneously samples the posterior distribution of the model parameters and does inference on the choice of covariates. The methodology is applied to simulated data and to a newly collected dataset from eBay with bids and covariates from 1000 coin auctions. We demonstrate that the Bayesian algorithm is very efficient and that the approximation error in the bid function has virtually no effect on the model inference. Both models fit the data well, but the Gaussian model outperforms the gamma model in an out-of-sample forecasting evaluation of auction prices. This article has supplementary material online.     

Modelling concurrency of events in on-line auctions via spatiotemporal semiparametric models

Summary.  We introduce a semiparametric approach for modelling the effect of concurrent events on an outcome of interest. Concurrency manifests itself as temporal and spatial dependences. By temporal dependence we mean the effect of an event in the past. Modelling this effect is challenging since events arrive at irregularly spaced time intervals. For the spatial part we use an abstract notion of 'feature space' to conceptualize distances among a set of item features. We motivate our model in the context of on-line auctions by modelling the effect of concurrent auctions on an auction's price. Our concurrency model consists of three components: a transaction-related component that accounts for auction design and bidding competition, a spatial component that takes into account similarity between item features and a temporal component that accounts for recently closed auctions. To construct each of these we borrow ideas from spatial and mixed model methodology. The power of this model is illustrated on a large and diverse set of laptop auctions on eBay.com. We show that our model results in superior predictive performance compared with a set of competitor models. The model also allows for new insight into the factors that drive price in on-line auctions and their relationship to bidding competition, auction design, product variety and temporal learning effects.     

Integrated-Quantile-Based Estimation for First-Price Auction Models

This article considers nonparametric estimation of first-price auction models under the monotonicity restriction on the bidding strategy. Based on an integrated-quantile representation of the first-order condition, we propose a tuning-parameter-free estimator for the valuation quantile function. We establish its cube-root-n consistency and asymptotic distribution under weaker smoothness assumptions than those typically assumed in the empirical literature. If the latter are true, we also provide a trimming-free smoothed estimator and show that it is asymptotically normal and achieves the optimal rate of Guerre, Perrigne, and Vuong (2000). We illustrate our method using Monte Carlo simulations and an empirical study of the California highway procurement auctions. Supplementary materials for this article are available online.     

Bayesian comparison of private and common values in structural second-price auctions

Private and common values (CVs) are the two main competing valuation models in auction theory and empirical work. In the framework of second-price auctions, we compare the empirical performance of the independent private value (IPV) model to the CV model on a number of different dimensions, both on real data from eBay coin auctions and on simulated data. Both models fit the eBay data well with a slight edge for the CV model. However, the differences between the fit of the models seem to depend to some extent on the complexity of the models. According to log predictive score the IPV model predicts auction prices slightly better in most auctions, while the more robust CV model is much better at predicting auction prices in more unusual auctions. In terms of posterior odds, the CV model is clearly more supported by the eBay data.     

The determinants of price in online auctions: more evidence from unbalanced panel data

This study provides an alternative approach that takes account of the unobserved effects of each seller under a sample selection framework while using online auction data. We use data collected from Yahoo! Kimo Auction (Taiwan) to demonstrate that earlier empirical results of online auction studies may be biased due to violating the assumption of independence of the error terms between sample observations. Empirical findings show that seller reputation is no longer as the most important factor for buyers to bid on items, while the sample data confirm the unobserved heterogeneity of sellers and sample selection problem.     

风险规避情形下第一价格拍卖的非参数估计方法

在拍卖实证研究的大量文献中,越来越多的证据表明投标人更趋向于风险规避,然而目前拍卖计量方法通常只考虑风险中性的情形。针对这一问题,推广了针对第一价格拍卖的非参数估计方法,给出了多个风险规避参数估计量来处理风险规避的情形,并总结了相应的估计过程。为验证估计效果,蒙特卡罗模拟实验被用于进行分析和评价。实验结果表明,无论是对风险规避参数,还是私有估值的估计,总体上都能取得不错的效果,验证了方法的有效性,同时为风险规避参数估计量的选择提供了一些指导性建议。