城市特征对城市游客满意度的影响——基于Probit模型的定量分析

游客满意度是衡量城市旅游发展的重要指标之一,掌握和控制游客满意度的影响因素对城市旅游发展和管理越来越关键。国内外研究综述表明,城市旅游的游客满意度影响因素及其关系的定量研究尚不多见,尤其对城市特征因素的研究。城市特征是城市旅游的必要组成,作为客观因素对游客满意度的影响不言而喻。文章运用文献分析法、问卷调查法和二元Probit离散选择模型计量法,以长沙市477名游客的现场调查数据为基础,对城市特征、个人特征和旅游动机三大变量下10个影响因素与游客满意度之间的假设关系进行检验。研究结果显示,修正后的游客满意度影响因素Probit模型具有统计学意义,6个研究假设得到验证;研究结论表明,城市社会发展水平、城市绿化水平、环境保护水平、旅游资源丰富程度、游客月收入、游客是否主动到访等因素对城市旅游的游客满意度产生显著的正面影响,其中,游客主动到访因素对游客满意度的影响作用最大,城市社会发展水平影响作用最小。研究结论一定程度上有利于促使城市管理者更多关注城市发展与旅游质量,最后,从满意度改善系统、城市要素建设、城市旅游资源开发和游客营销引导等方面提出政策建议。     

How Much of the Gender Difference in Child School Enrolment Can Be Explained? Evidence from Rural India

There are significant gender differences in child schooling in the Indian states though very few studies explain this gender difference. Unlike most existing studies we take account of the implicit and explicit opportunity costs of schooling and use a bivariate probit model to jointly determine a child's participation in school and market jobs. Results obtained from the World Institute of Development Economics Research (WIDER) villages in West Bengal suggest that indicators of household resources, parental preferences, returns to and opportunity costs of domestic work significantly affect child school enrolment. While household resources have similar effects on enrolment of boys and girls, other arguments tend to explain a part of the observed gender difference. Even after taking account of all possible arguments, there remains a large variation in gender differences in child schooling that cannot be explained by differences in male and female characteristics in our sample.     

贫困农户信贷可获得性影响因素的实证研究

本文运用判别抽样法,定点选取了16个省（市）国定贫困县中的1503户暂时性贫 困农户为对象,对其经济特征、信贷需求及贷款可获得性进行了调查分析,在此基础上,运用 Probit模型对其贷款可获得性影响因素进行了实证检验。调查发现：大多数暂时性贫困农户均 有信贷需求,且以中短期和小额为主,用途以消费为主,生产次之,信贷可获得性整体偏低, 且正规信贷获取难度大于非正规信贷。实证结果显示：户主受教育程度、家庭收入、社会关系 对贫困农户正规信贷和整体信贷可获得性均有正向影响;家庭社会关系及区域影响力对非正 规信贷可获性有显著正向影响,金融机构物理距离对贫困农户信贷可获得性产生了一定的排斥 效应。可见,户主文化程度、家庭收入、社会关系、金融网点已成为我国贫困农户信贷可获得 性的现实制约因素。     

A posteriori ratemaking using bivariate Poisson models

Recently, different bivariate Poisson regression models have been used in the actuarial literature to make an a priori ratemaking taking into account the dependence between two types of claims. A natural extension for these models is to consider a posteriori ratemaking (i.e. experience rating models) that also relaxes the independence assumption. We introduce here two bivariate experience rating models that integrate the a priori ratemaking based on the bivariate Poisson regression models, extending the existing literature for the univariate case to the bivariate case. These bivariate experience rating models are applied to an automobile insurance claims data-set to analyse the consequences for posterior premiums when the independence assumption is relaxed. The main finding is that the a posteriori risk factors obtained with the bivariate experience rating models are significantly lower than those factors derived under the independence assumption.     

Housing Loan Patterns Toward Minority Borrowers in Mississippi: Analysis of Some Micro Data Evidence of Redlining

We apply a cross-regional probit analysis to examine the existence or otherwise of the discriminatory practice of “redlining” in housing loan lending practices of banks in Mississippi. Data on loan denial rates across three Mississippi regions of Tri-County Metro-Jackson, Southern Mississippi Corridor, and the Northern district are studied to determine the pattern of bank lending activities regarding owner-occupied housing loan extensions to minority members of Mississippi's population. The purpose is to determine the degree to which the banks have or have not observed fair and equitable lending practices toward minority borrowers, relative to the members of the majority population. The results suggest that there is a consistently high denial rates against minorities. The results indicate strong evidence of redlining practices in housing loan decisions, which contribute to the growth of racial segregation in the state.

Goodness-of-fit tests for Poisson count time series based on the Stein–Chen identity

To test the null hypothesis of a Poisson marginal distribution, test statistics based on the Stein–Chen identity are proposed. For a wide class of Poisson count time series, the asymptotic distribution of different types of Stein–Chen statistics is derived, also if multiple statistics are jointly applied. The performance of the tests is analyzed with simulations, as well as the question which Stein–Chen functions should be used for which alternative. Illustrative data examples are presented, and possible extensions of the novel Stein–Chen approach are discussed as well.     

农地流转背景下新疆农户残膜回收机械化技术采纳分析

[目的]新疆是我国农膜施用和农膜残留最严重的地区。基于农户调查数据,分析农户残膜回收方式和残膜回收机械化技术采纳,重点研究农地流转对农户残膜回收机械化技术采纳的影响,以期为新疆及全国农膜残膜污染治理提供了有益借鉴。[方法]文章采用描述性统计分析农户残膜回收方式和处置行为,运用Probit回归模型实证分析农地流转对农户残膜回收机械化技术采纳的影响。[结果]新疆农膜残膜回收方式目前主要依靠人工捡膜,而农地流转促进了农户采用残膜回收机械化技术;户主个体特征和政策因素是影响农户残膜回收的主要因素,而人均农业收入、耕地特征、残膜回收技术培训和订单农业生产对农户残膜回收机械化技术采纳产生重要影响。[结论]随着农业劳动力成本上升和耕地规模经营,人工残膜回收方式不具有可持续性,而农地流转后耕地规模经营有助于残膜回收机械化技术的应用,提高了耕地残膜回收率。因此,有序推进农地流转规模经营,促进农户残膜回收机械化技术采纳是未来残膜污染治理的必然选择。     

Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model

In this paper we investigate the ability of a number of different ordered probit models to predict ratings based on firm-specific data on business and financial risks. We investigate models which are based on momentum, drift and ageing, and compare them with alternatives which take the initial rating of the firm and its previous actual rating into account. Using data on US bond issuing firms, as rated by Fitch, over the years 2000 to 2007, we compare the performances of these models for predicting the ratings both in-sample and out-of-sample using root mean squared errors, Diebold-Mariano tests of forecast performance and contingency tables. We conclude that both initial and previous states have a substantial influence on rating prediction.     

二元Copula函数的投资组合模型选择

Coplua模型是组合投资风险评估中常用模型,它具有多种不同的类型,模型选择的好坏对风险评估结果具有至关重要的影响.本文主要比较了二元正态Copula模型和二元t-Copula模型对中国股市数据拟合的优劣程度.针对这两种模型,利用上证综指、深证成指、上证基金、深证基金、东风汽车、中国石化、宝钢股份和万家乐的日收盘价数据估计相应的参数得到相应的拟合分布,然后分别与经验Copula函数作比较,通过计算拟合分布与经验分布之间的距离,得出二元t-Cop-ula函数能更好地拟合两组投资组合的日收益率数据的结论.